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Top 10 Best Satellite Simulation Software of 2026

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Top 10 Best Satellite Simulation Software of 2026

Ranking of satellite simulation software for orbital modeling and mission analysis, with technical comparisons of STK, FreeFlyer, Orekit.

19 min readUpdated Editorially scoped research
How we ranked these tools
01Editorial Scope

Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.

02Source Research

Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.

Read our full methodology →

Framework: Features 40% · Ease 30% · Value 30%

Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy

Satellite simulation software tools sit between orbital theory and operational planning by providing physics-based orbit propagation, sensor and station pass modeling, and repeatable scenario execution. This ranked list targets analysts and operators who need verifiable modeling fidelity and integration depth, comparing simulation engines, APIs, and data-model fit so teams can choose the right workflow for mission analysis and automation.

Poliastro is the best fit overall if you want scriptable orbit propagation and custom perturbation studies in Python, while OpenSATKIT suits teams needing repeatable scenario orchestration with access checks, and if you’re budget-focused then SatNOGS works best when the real priority is ground pass scheduling and downlink data.

Editor’s top 3 picks

Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.

Editor pick
1

Poliastro

Force-model customization in Python for integrating state vectors under user-defined accelerations.

Best for: Fits when mission analysts need scriptable orbit propagation and custom perturbation studies in Python.

2

OpenSATKIT

Editor pick

Configuration-driven scenario orchestration that supports repeatable batch propagation runs.

Best for: Fits when teams need repeatable, scriptable scenario orchestration for orbital studies and access checks.

3

STK

Editor pick

STK scenario timelines tie orbital propagation outputs to access and link budget evaluations in one model.

Best for: Fits when teams need scenario-linked orbit, access, and link analysis with repeatable automation.

Comparison Table

1
PoliastroBest overall
SMB
9.5/10
Overall
2
vertical specialist
9.2/10
Overall
3
enterprise
8.9/10
Overall
4
8.6/10
Overall
5
8.3/10
Overall
6
vertical specialist
7.9/10
Overall
7
API-first
7.7/10
Overall
8
enterprise
7.3/10
Overall
9
vertical specialist
7.0/10
Overall
10
open-source
6.7/10
Overall
#1

Poliastro

SMB

Interactive orbital mechanics toolbox for Python astrodynamics.

9.5/10
Overall
Features9.2/10
Ease of Use9.7/10
Value9.7/10
Standout feature

Force-model customization in Python for integrating state vectors under user-defined accelerations.

Poliastro is built for hands-on orbital mechanics work where the workflow is scripted in Python, not assembled from a closed interactive mission tree. Orbit propagation is handled by calling propagation routines that numerically integrate state vectors under configurable force models. The library supports common inputs such as elements and epoch handling so scenario timelines can be run as repeatable computations.

The main tradeoff is that Poliastro concentrates on the orbit dynamics core rather than full mission engineering like integrated link budget toolchains or ground segment scheduling automation. It fits best when teams need batch Monte Carlo propagation, covariance-style analyses, or event-driven evaluations driven by custom perturbation models created in code. A typical usage is generating ground tracks for candidate orbits while swapping force model parameters and integrator settings across many trials.

Pros
  • +Python scripting enables batch propagation workflows without GUI limitations
  • +Configurable force model inputs let custom perturbations plug into propagation
  • +Orbit element and epoch utilities simplify repeatable scenario setup
  • +Ground track and observation geometry outputs support mission-style diagnostics
Cons
  • –Thinner coverage for end-to-end mission engineering beyond orbit dynamics
  • –Integrator tuning is required to manage accuracy and runtime for high-fidelity models
  • –No built-in enterprise governance features like RBAC and audit logs
  • –Coupling to higher-fidelity ecosystem tools requires custom conversion work
Use scenarios
  • Orbit analysts and mission designers

    Batch propagate candidate orbits with custom forces

    Comparable delta-V and geometry outputs

  • Research teams running Monte Carlo

    Study sensitivity to element uncertainty

    Ranked uncertainty drivers

Show 2 more scenarios
  • GNC and guidance engineers

    Validate simplified dynamics for control studies

    Repeatable dynamics regression

    Compare low-fidelity perturbations against higher-fidelity cases by selecting different acceleration models.

  • Software engineers in federated sims

    Provide ephemeris generation as a service step

    Deterministic ephemeris generation

    Embed propagation code in a larger pipeline to produce time-ordered state outputs for downstream components.

Best for: Fits when mission analysts need scriptable orbit propagation and custom perturbation studies in Python.

#2

OpenSATKIT

vertical specialist

Open-source satellite software framework that supports simulation, flight software development, and mission operations workflows.

9.2/10
Overall
Features9.0/10
Ease of Use9.2/10
Value9.5/10
Standout feature

Configuration-driven scenario orchestration that supports repeatable batch propagation runs.

OpenSATKIT is most useful when mission designers need scripted scenario assembly that can be re-run with controlled configuration changes. The workflow favors reproducibility by treating scenario setup as artifacts that can be regenerated for batch studies and regression checks. The integration depth is strongest when the simulation chain already uses compatible ephemeris and event timing conventions.

A practical tradeoff appears when teams expect deep, mission-wide automation like STK-style end-to-end object building from a single click path. OpenSATKIT fits best for users who can define their own simulation topology and then iterate on propagation settings, event logic, and output generation across many scenario variants.

Pros
  • +Scenario runs stay reproducible through configuration-driven orchestration
  • +Modular propagation and environment modeling supports iterative refinement
  • +Batch-ready workflow helps compare many scenario variants
Cons
  • –GUI-driven object setup is less comprehensive than enterprise tools
  • –Advanced force-model tuning needs more user discipline
Use scenarios
  • Mission designers

    Iterate access windows over many cases

    Faster pass comparisons

  • Flight dynamics analysts

    Swap force-model assumptions systematically

    Lower analysis variance

Show 2 more scenarios
  • Software engineers

    Embed propagation in internal tools

    More integration control

    The workflow is organized around configuration artifacts that can be generated and consumed by external automation.

  • Research groups

    Run Monte Carlo dispersion batches

    Cleaner ensemble datasets

    Repeatable scenario execution makes it easier to run dispersion studies and collect consistent outputs.

Best for: Fits when teams need repeatable, scriptable scenario orchestration for orbital studies and access checks.

#3

STK

enterprise

Systems Tool Kit models multi-domain missions including satellites, aircraft, and ground systems with physics-based astrodynamics and visualization.

8.9/10
Overall
Features8.8/10
Ease of Use8.8/10
Value9.2/10
Standout feature

STK scenario timelines tie orbital propagation outputs to access and link budget evaluations in one model.

STK supports non-real-time batch simulation and scenario-based event playback, which helps teams run repeatable studies like access windows, eclipse, and link margin across many parameter sets. Force modeling includes geopotential options like spherical harmonics, and it can incorporate atmospheric drag and solar radiation pressure when spacecraft or environment parameters are specified for the scenario. Ground segment analysis includes elevation masks and ground station pass computations that can be scheduled along a timeline for constellation and single-spacecraft studies.

A key tradeoff is that deep customization often depends on STK scripting or custom components rather than configuration alone, which adds setup time for organizations with strict change-control processes. STK fits well when an engineering team needs a single modeling workspace that links orbit propagation outputs to sensor tasking, access evaluation, and link budget metrics for mission reviews.

Pros
  • +One scenario timeline connects propagation, access, and link budget outputs
  • +Extensible scripting and custom object creation for domain-specific workflows
  • +High-detail force model configuration for perturbation-aware studies
  • +Batch and event-driven analysis patterns for repeated mission studies
Cons
  • –Deep customization often requires scripting and scenario refactoring
  • –Modeling accuracy depends heavily on correctly specified force and environment parameters
  • –Large scenarios can be slower when many objects and high-fidelity settings are enabled
  • –Integration work can add governance overhead when multiple teams maintain extensions
Use scenarios
  • Mission designers

    Evaluate access and link during trades

    Faster trade study iterations

  • Ground segment engineers

    Plan station passes and visibility

    Operationally usable pass schedules

Show 2 more scenarios
  • GNC and dynamics analysts

    Quantify environment-driven performance changes

    More realistic coverage margins

    Model atmospheric drag and solar radiation pressure effects that influence coverage and timing.

  • Software automation leads

    Automate scenario runs via APIs

    Reduced manual run effort

    Drive repeatable studies by automating scenario setup, execution, and result extraction workflows.

Best for: Fits when teams need scenario-linked orbit, access, and link analysis with repeatable automation.

#4

COMSOL Multiphysics

enterprise

Multiphysics simulation platform used for satellite thermal, structural, RF, and radiation-related subsystem modeling.

8.6/10
Overall
Features8.4/10
Ease of Use8.6/10
Value8.8/10
Standout feature

Native multiphysics model tree that couples environment-driven loads into FEM stress and thermal fields in one run.

COMSOL Multiphysics can simulate satellite-relevant physics through a single multiphysics workflow that couples orbital environment inputs to structural, thermal, and electromagnetic models. For mission analysis tasks, it is most practical when high-fidelity dynamics and time-varying loads need to feed into FEM-grade stress, heat, and deformation results across a scenario timeline.

Pros
  • +Tight coupling between time-varying orbital loads and FEM structural response
  • +Thermal-structural coupling supports radiator heating and thermoelastic deformation
  • +Batch studies with parametric sweeps support dispersion-like scenario runs
  • +Scripting and model API support automation for repeatable campaign execution
Cons
  • –Orbit propagation and tracking workflows are not its primary specialization
  • –High-fidelity setups require careful solver and meshing choices

Best for: Fits when mission teams need high-fidelity thermal and structural response to orbital environment time histories.

#5

Aerospace Blockset

enterprise

Simulink block library for aerospace simulation including spacecraft dynamics and flight software test scenarios.

8.3/10
Overall
Features8.3/10
Ease of Use8.0/10
Value8.5/10
Standout feature

Unified Simulink-style signal flow that co-simulates orbital motion with attitude control plant, sensors, and actuators.

Aerospace Blockset in MATLAB converts physical models into spacecraft and subsystem simulations by assembling block diagrams for dynamics, guidance, and control. It supports orbit propagation and force modeling inputs as simulation-ready signals that can run through a scenario timeline with repeatable results.

Pros
  • +Block-diagram integration of orbital motion and GNC control loops in one simulation model
  • +MATLAB workspace integration enables scripted scenario generation and batch runs
  • +Sensor and actuator models plug into the same signal network as guidance and dynamics
  • +Supports Monte Carlo style variation using parameter sweeps over model inputs
Cons
  • –High-fidelity propagation requires careful force model selection and tuning for repeatability
  • –Building constellation workflows can take more engineering than a dedicated mission analysis tool

Best for: Fits when spacecraft dynamics and GNC logic need a single model-based simulation with automation hooks.

#6

SaVoir

vertical specialist

Mission planning and satellite observation simulation software for Earth observation and sensor tasking analysis.

7.9/10
Overall
Features7.8/10
Ease of Use8.1/10
Value8.0/10
Standout feature

Scenario-driven execution keeps orbit and contact geometry synchronized across propagation, passes, and timeline events.

SaVoir focuses on satellite simulation workflows that combine orbital propagation with mission planning tasks like ground coverage and event timing. It is distinct for how scenario timelines drive analysis runs and for how orbit, attitude, and contact geometry are kept aligned during those runs.

Pros
  • +Scenario timeline ties propagation outputs to access events without manual re-mapping
  • +Ground track and access window calculations support station pass reviews
  • +Repeatable scenario configurations fit batch runs and regression testing
  • +Exported results support integration with mission analysis spreadsheets and scripts
Cons
  • –High-fidelity dynamics setup requires careful selection of force model components
  • –Some constellation-level automation needs extra scripting for topology generation

Best for: Fits when teams need repeatable orbital propagation plus ground access analysis driven by scenario timelines.

#7

Nyx Space

API-first

Spaceflight dynamics software platform focused on orbit determination, trajectory design, and mission analysis.

7.7/10
Overall
Features7.5/10
Ease of Use7.9/10
Value7.7/10
Standout feature

Scenario-driven job runs that keep propagation settings and access-event outputs aligned across batch constellation studies.

Nyx Space focuses on satellite simulation workflows that connect orbit dynamics to mission-level analysis through a scenario timeline workflow. The tool supports non-real-time batch simulation for constellation studies where propagation steps, events, and output products must be repeatable across many runs.

Pros
  • +Scenario timeline ties orbit propagation outputs to mission tasks in one configuration
  • +Batch execution supports high-throughput constellation Monte Carlo style runs
  • +Event-driven modeling reduces manual coordination between passes and maneuver logic
  • +API-friendly automation fits repeatable studies and scripted design iterations
Cons
  • –Attitude and payload modeling depth can lag specialized GNC and sensor simulators
  • –Complex models require careful configuration discipline to avoid inconsistent run outputs
  • –Interoperability with external ephemeris formats may require preprocessing steps
  • –Large scenario graphs can become slower to iterate when output granularity is high

Best for: Fits when teams need repeatable, automated mission analysis across many orbit scenarios without building custom orchestration.

#8

MONTE

enterprise

Mission analysis toolkit from JPL for trajectory design, navigation analysis, and high-precision space mission simulation.

7.3/10
Overall
Features7.6/10
Ease of Use7.1/10
Value7.2/10
Standout feature

Built-in mission-analysis workflow output generation tied to scenario timelines for repeated access and ground-track studies.

MONTE from NASA JPL is a satellite simulation software that focuses on mission analysis and orbit propagation workflows with Monte Carlo-style studies. The software supports scenario-driven batch runs for force modeling, event timing, and ground track outputs used for access-window studies.

Pros
  • +Scenario-driven batch runs with event timing for access-window analysis
  • +Deterministic propagation settings per run support repeatable study design
  • +Strong support for LEO and higher regimes through configurable force models
  • +Outputs aligned to mission analysis workflows like ground track and visibility
Cons
  • –Workflow depth favors scripted study setup over interactive exploration
  • –Integration with external tools can require custom adapters for data formats

Best for: Fits when teams need repeatable orbit propagation batches for coverage studies with controlled run settings.

#9

SatNOGS

vertical specialist

Open-source satellite ground station network with orbit prediction, pass scheduling, and telemetry decoding.

7.0/10
Overall
Features6.8/10
Ease of Use7.2/10
Value7.1/10
Standout feature

Pass scheduling and telemetry acquisition across a federated ground-station network, with decoded results archived for repeatable validation.

SatNOGS operates a distributed network of ground stations that collects telemetry from real satellites and archives it for later use. It provides an end-to-end workflow for scheduling observations, decoding downlink data, and managing pass-level operations across many stations.

Pros
  • +Distributed ground-station scheduling with pass-level observation tracking
  • +Telemetry decoding pipeline supports multiple receiver and decoder workflows
  • +Archived observation outputs create replayable evidence for scenario validation
  • +Community extensibility for new targets and station participation
Cons
  • –Simulation-grade orbital propagation and scenario orchestration are not the core focus
  • –Higher complexity when integrating custom RF front ends and decoder logic
  • –Operational workflows depend on community station coverage and timing
  • –Link budget and high-fidelity force modeling are limited compared with flight dynamics tools

Best for: Fits when real downlink archives and automated ground-station access matter more than high-fidelity orbit propagation.

#10

Basilisk

open-source

Basilisk is an open-source framework for spacecraft dynamics, guidance, navigation, and control simulation.

6.7/10
Overall
Features6.9/10
Ease of Use6.7/10
Value6.6/10
Standout feature

Event-driven scenario execution that ties propagation outputs to access windows and eclipse-linked constraints in one run.

Basilisk by Hanspeter Schaub is a satellite simulation tool aimed at orbit and mission analysis workflows that mix simple propagators with scenario-driven event handling. It supports core orbital mechanics needed for LEO through higher regimes, including perturbation force modeling such as J2 oblateness, atmospheric drag, and solar radiation pressure.

Pros
  • +Scenario timeline control for repeatable non-real-time batch simulations
  • +Perturbation force modeling including J2, drag, and solar radiation pressure
  • +Ground track and pass geometry outputs suitable for access-window checks
  • +Eclipse timing support for power-related mission logic
Cons
  • –Limited federation or co-simulation integration options compared with enterprise tools
  • –Orbit regime coverage can feel uneven outside the tool’s primary dynamics focus
  • –Attitude and sensor chain modeling depth is thinner than dedicated dynamics stacks
  • –Extensibility relies more on workflow conventions than published API contracts

Best for: Fits when small teams need scripted batch orbit and access-window analysis with perturbations.

Conclusion

After evaluating 10 satellite simulation software, Poliastro stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.

Our Top Pick
Poliastro

Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.

How to Choose the Right Satellite Simulation Software

We separate options in satellite simulation software by how their modeled fidelity, workflow automation, and interoperability with your scenario data affect day-to-day run-to-run trust. In practice, the biggest differences show up in Propagation model choices and Scenario automation depth.

Propagation model choices

Why it matters

Propagation model choices determine how credibly results track real orbital and environment effects for your use cases. If you need sensitivity studies, this label also affects whether the tool can reproduce the same conclusions across runs.

How to check

Load a sample scenario and compare results with at least two different propagation options the demo offers, then observe how outputs change. Ask for an example that includes environmental effects and confirm where those effects are configured.

Scenario automation depth

Why it matters

Scenario automation depth matters when you run many cases and need consistent inputs, repeatable outputs, and predictable throughput. For teams, it also reduces the chance that manual edits silently change results.

How to check

Try running a batch of multiple scenarios that differ by one parameter (e.g., initial condition). Check whether the workflow can be parameterized via a script or API and whether you can export run definitions and outputs programmatically.

Scenario data import and export coverage

Why it matters

Data import and export coverage affects whether you can reuse your existing ephemeris, objects, and schedules without reformatting everything by hand. It also impacts traceability when you need to compare runs months later.

How to check

In the demo, import an external scenario package or sample data you already have, then export the same scenario and validate that key fields still map correctly. Ask what file formats and field mappings are supported and request a before/after example.

Event timeline and task scheduling

Why it matters

Event timeline and task scheduling control when actions happen and how outputs line up with your operational concept of time. Weak control here leads to misaligned passes, conflicts, or timing-dependent measurements.

How to check

Edit a scenario with at least three time-based events (e.g., start, maneuver, observation window) and confirm ordering and time-step handling. Ask how the tool handles time ordering when two events are close together and verify with an example output.

Uncertainty and dispersion tools

Why it matters

Uncertainty and dispersion tools matter if you need risk-informed outputs instead of single deterministic tracks. This label also helps you test whether your analysis can represent variability in initial conditions and environment.

How to check

Run a case with uncertainty inputs and request to see how the tool produces distributions or variability metrics. Confirm what outputs you can export for downstream analysis and whether you can reproduce the same dispersion with a fixed random seed.

Verification and run traceability

Why it matters

Verification and run traceability determine whether you can defend results and quickly reproduce them. If outputs change, you need clear records of configuration, inputs, and execution settings.

How to check

Export a run report for a scenario that you modify, then compare the two reports to verify they capture all relevant configuration changes. Ask whether the tool logs provenance (inputs, versions, configuration) and how you retrieve it later.

Red flags

  • Results change without a clear way to capture what configuration produced them
  • Batch runs require manual re-entry of scenario details
  • Environmental effects are mentioned but not actually configurable in the demo workflow

Fit by situation

  • If you do parameter sweeps and want consistent conclusions across many cases, prioritize Scenario automation depth.
  • If your analysis depends on realistic environment sensitivity, prioritize Propagation model choices.
  • If you must reuse existing scenario files and later reproduce outcomes, prioritize Scenario data import and export coverage.

Frequently Asked Questions About Satellite Simulation Software

Which tool best fits custom perturbation acceleration models written in Python for orbit propagation?
Poliastro fits mission analysts who need force-model customization directly in Python. It converts published perturbations into integrator-ready acceleration models and outputs mission geometry such as ground tracks. STK can also run configurable force models but keeps the modeling workflow inside its scenario model and automation layer rather than a Python-first acceleration API.
How does event-driven scenario timing differ between STK, Basilisk, and OpenSATKIT?
STK ties orbit propagation outputs to its scenario timeline, then drives sensor access and link budget workflows by event passes. Basilisk uses event-driven scenario execution so propagation outputs can trigger access windows and eclipse-linked constraints in the same run. OpenSATKIT focuses on scenario orchestration built from modular components, so event timing and propagation settings are assembled as a repeatable workflow rather than managed as a single integrated GUI timeline.
When is Monte Carlo dispersion analysis practical inside COMSOL Multiphysics compared with flight-dynamics tools?
COMSOL Multiphysics fits cases where time-varying loads from orbital environment inputs must drive FEM-grade stress, thermal fields, and deformation. It can run batch studies driven by propagation outputs, which is useful when dispersion changes the environment time history fed into structural and thermal solvers. Poliastro, STK, and MONTE prioritize propagator-centric dispersion workflows and do not replace FEM pipelines for coupled thermo-structural response.
What breaks if a mission uses a TLE-first workflow that also needs high-fidelity force modeling and consistent event outputs?
A TLE-first workflow can break traceability when event-driven access windows depend on force-model fidelity and time-varying geometry. STK handles this by binding propagation outputs to scenario-linked access and link budget evaluations on the same timeline. OpenSATKIT and SaVoir help by keeping propagation inputs and scenario timelines synchronized, while Poliastro and Basilisk can model perturbations in code or scripts but require careful integration to keep access and derived constraints consistent across batches.
How do Nyx Space and MONTE support large constellation studies that require repeatable batch outputs?
Nyx Space supports non-real-time batch simulation via scenario-driven job runs that keep propagation settings aligned with access-event outputs across many runs. MONTE from NASA JPL similarly emphasizes repeatable simulation batches that generate mission-analysis outputs for access-window and ground-track studies from controlled run settings. STK also supports automation, but its strength is scenario-linked orbit, access, and link workflows inside a unified model rather than batch-job orchestration as the primary workflow shape.
Which tool provides the strongest data-migration workflow for ephemeris and mission data exchange through standardized spacecraft analysis artifacts?
STK supports ephemeris exchange workflows and integration paths for tying propagation outputs to external analysis models. SaVoir and OpenSATKIT focus on scenario configurations that export results for downstream reporting, which makes migration practical when the bottleneck is scenario assembly and repeatability. COMSOL Multiphysics migration is more effective when the target domain is coupled physics, because it centers on model-tree coupling of environment-driven loads into FEM outputs.
How do orbit-access computations differ between SaVoir, Basilisk, and SATNOGS when ground contacts must align with operational constraints?
SaVoir keeps orbit, attitude, and contact geometry synchronized through scenario-driven execution so ground coverage and access windows reflect the same timeline inputs. Basilisk ties propagation outputs to access-window logic and eclipse-linked constraints in an event-driven run. SatNOGS differs because it validates simulation assumptions against real pass scheduling and archived downlink telemetry from distributed ground stations, so operational alignment can be tested against observed access patterns.
What security and administration capabilities matter for SSO and audit logging in this software category?
Security and SSO are typically governed by the deployment environment and integration layer rather than by an orbit-propagation engine itself. STK’s ecosystem and integration paths are the usual entry point for enterprise admin controls, while OpenSATKIT and Nyx Space are commonly used in automation-oriented workflows where access control is handled by the orchestrator and data pipeline. For auditability, batch workflows in MONTE and OpenSATKIT tend to rely on traceable run settings and repeatable scenario configurations rather than a built-in centralized RBAC and audit log model.
How is extensibility handled across Poliastro, STK, and Aerospace Blockset when new sensors or dynamics must be added?
Poliastro extends extensibility through user-defined accelerations written in Python and reused across propagation calls. STK supports extensibility through scripting and custom objects so new modeling elements can be incorporated into scenario timelines and event handling. Aerospace Blockset extends by assembling block diagrams so dynamics, guidance, sensor models, and actuator plant components can be wired into a unified simulation with repeatable signal-flow execution.

Tools reviewed

Primary sources checked during evaluation.

Referenced in the comparison table and product reviews above.

Commissioned Research

Funding and competing interests

This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.

They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.

Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →

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