GITNUXSOFTWARE ADVICE
Art DesignTop 10 Best AI Upscaling Video Software of 2026
Ranked comparison of ai upscaling video software tools with criteria and tradeoffs for sharper results, including TensorPix, Cutout Pro, Aiseesoft.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
TensorPix is the best fit for production teams that need consistent offline AI upscaling across many clips, whereas Cutout Pro is a better alternative when you want repeatable enhancement on compressed footage with acceptable temporal stability.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
TensorPix
Temporal consistency tuning for motion-heavy footage reduces frame-to-frame flicker in upscaled outputs.
Built for fits when production teams need consistent offline AI upscaling across many clips..
Cutout Pro
Editor pickCutout-first restoration workflow that pairs cutout preparation with sharpness-preserving upscaling outputs.
Built for fits when offline teams upscale many compressed videos with consistent settings and acceptable temporal stability..
Aiseesoft Video Enhancer
Editor pickIntegrated enhancement workflow that pairs upscaling with denoise and sharpen tuning before exporting a codec-selected final file.
Built for fits when editors need fast offline upscaling with repeatable export settings for batches of similar footage..
Comparison Table
TensorPix
SMBOnline AI video upscaling and enhancement service.
Temporal consistency tuning for motion-heavy footage reduces frame-to-frame flicker in upscaled outputs.
TensorPix is built around a queue-based upscaling workflow that turns uploaded video into upscaled exports suitable for later editorial review. The system emphasizes temporal consistency by focusing on interframe coherence to reduce small frame-to-frame changes that show up as flicker. Batch throughput is a core fit signal since the tool is usable for multi-asset processing instead of only single-shot demos.
A tradeoff appears in motion-heavy footage where any reference-based restoration approach can still introduce detail hallucination or slight edge instability. TensorPix fits best when a production team can validate results on representative sequences and then process the full batch with consistent settings.
- +Batch render queue fits series re-exports and multi-clip workflows
- +Temporal flicker reduction improves perceived stability on motion
- +Source footage analysis focuses reconstruction on actual content
- +Repeatable settings support consistent output across asset batches
- –Motion-heavy scenes can still show minor edge instability
- –Requires careful parameter selection to avoid over-smoothing
- –Preview checks may not reveal codec-specific artifacts in exports
- –Large projects depend on sustained GPU-backed throughput
Video editors
Upscale client archive footage
Faster turnaround on deliverables
Content pipelines teams
Batch re-export episodes
Higher throughput across episodes
Show 2 more scenarios
Studio post-production
Improve compression artifact mitigation
Cleaner frames for grading
Reduce block artifacts and ringing in previously encoded footage before finishing.
Media librarians
Restore legacy clip libraries
Unified library resolution
Apply repeatable upscaling to large archives for downstream playback.
Best for: Fits when production teams need consistent offline AI upscaling across many clips.
Cutout Pro
SMBAI-powered video and photo enhancement platform.
Cutout-first restoration workflow that pairs cutout preparation with sharpness-preserving upscaling outputs.
Cutout Pro targets post-production use where source footage analysis drives the upscaling pass and output quality is checked on preview renders before full jobs run. The workflow is built around GPU inference, so VRAM limits and inference latency become the practical ceiling for maximum resolution and frame size. Batch processing is the default shape, which fits encoder pipeline stages that follow deinterlacing and codec transcode steps.
A key tradeoff is that higher resolution multipliers can increase detail hallucination risk, especially on low-motion scenes with compression artifacts. Cutout Pro fits best when offline render queue control is available, such as small teams delivering multiple episodes with consistent settings and re-render rules for failed frames.
- +Batch video upscaling supports consistent settings across long queues
- +GPU-based inference reduces render time versus CPU-only workflows
- +Preview render steps help validate sharpness before final exports
- +Edge handling reduces softness on downscaled inputs
- –Temporal flicker can appear on fast motion without careful settings
- –VRAM limits restrict maximum frame size per run
- –Compressed sources with heavy block artifacts need preprocessing
- –Limited control depth for advanced alignment and interframe tuning
Video editors
Restore downscaled clips for export
Cleaner looking previews and masters
Media localization teams
Upscale multi-episode source libraries
Faster delivery with fewer re-renders
Show 2 more scenarios
Content ops teams
Regenerate degraded archives
More usable archive assets
Re-rendering with resolution multipliers reduces softness in legacy footage.
Independent filmmakers
Improve online viewing quality
Sharper uploads with less blur
Upscaling increases detail retention for small screens and player scaling.
Best for: Fits when offline teams upscale many compressed videos with consistent settings and acceptable temporal stability.
Aiseesoft Video Enhancer
SMBVideo enhancement software with upscaling, noise reduction, and deshake features.
Integrated enhancement workflow that pairs upscaling with denoise and sharpen tuning before exporting a codec-selected final file.
Aiseesoft Video Enhancer targets frame-level restoration and reconstruction for low-resolution footage, including denoise and sharpen style controls that affect visible texture. The workflow emphasizes selecting a source, choosing an upscaling target, and producing an enhanced output video without requiring script-based pipelines. Output controls include format and codec selection so enhanced results can be prepared for downstream editing or playback. The product is also positioned for inference-only use on a local workstation, which fits teams that want to process files without a render farm.
A concrete tradeoff is that deeper temporal consistency controls are limited, so fast motion can still show flicker or edge instability versus models tuned for interframe coherence. One usage fit is a batch processing pipeline where multiple clips from the same source class need consistent resolution multiplier and noise handling before a final review pass.
- +Batch-style processing lets teams queue multiple files for enhancement
- +Denoise and sharpening controls help tune artifact reduction results
- +Export settings allow codec and container choices for final outputs
- +Local inference workflow fits workstation-based post production
- –Limited temporal consistency controls can leave flicker in high motion
- –Model behavior can produce over-sharpening on already crisp sources
- –Advanced evaluation metrics and model diagnostics are not exposed
- –Thin automation surface beyond basic queue and preset-style configuration
Freelance video editors
Upscale client clips for delivery
Cleaner-looking renders for review
Local media teams
Batch process archives to higher resolution
Faster turnaround on archives
Show 2 more scenarios
Asset cleanup specialists
Reduce compression artifacts in videos
Fewer visible artifacts
Use artifact-reduction style controls to mitigate blocky edges and noise in compressed sources.
Small post-production houses
Prepare cutdowns for playback
Less conversion overhead
Upscale and export in one workflow to minimize handoffs between tools.
Best for: Fits when editors need fast offline upscaling with repeatable export settings for batches of similar footage.
Pixop
SMBAI video enhancement and upscaling platform for creators and businesses.
Offline render queue designed for batch asset processing with per-job output management for production handoffs.
Pixop is an AI upscaling video tool focused on improving perceived detail while keeping compression artifacts in check. The workflow is built around an offline render queue for batch processing, which fits production pipelines that need consistent output per asset.
The engine targets artifact reduction and edge-aware sharpening behavior during frame-by-frame inference, which helps when sources are low resolution or heavily compressed. Pixop also supports export choices that matter for codec compatibility and downstream editing, so the upscaled result can be re-encoded without breaking the pipeline.
- +Batch render queue fits offline production pipelines
- +Artifact reduction work helps limit ringing and block artifacts
- +Edge-aware sharpening improves readability on scaled footage
- +Export options help maintain codec compatibility for review and re-encode
- –Temporal consistency controls are limited for high motion sequences
- –Frame interpolation support is not positioned as a primary feature
- –VRAM limits can constrain throughput on long or high-resolution inputs
- –Fewer automation hooks than API-first command-line batch tool workflows
Best for: Fits when teams need offline upscaling with consistent batch output and controlled artifact reduction for re-encode workflows.
AVCLabs Video Enhancer AI
SMBAI-based video quality enhancer and upscaler.
Frame-focused enhancement with artifact reduction tuned for spatial clarity during offline batch upscaling.
AVCLabs Video Enhancer AI processes video by running AI-based upscaling and enhancement to produce higher resolution outputs with reduced compression artifacts. The workflow centers on batch processing for entire video files, with frame-level restoration intended to preserve edges and textures while denoising spatial regions.
Output tuning focuses on selecting an upscaling multiplier and exporting an enhanced render for offline review and final encode. The tool’s differentiator in this category is its inference-only video enhancement experience that targets practical visual sharpening rather than full frame-rate conversion.
- +Batch processing supports long video files in one pass
- +Upscaling multiplier selection is straightforward for repeatable outputs
- +Enhanced outputs reduce common compression artifact visibility
- +Local workstation workflow avoids external pipeline dependencies
- –No integrated temporal consistency controls for flicker-prone sources
- –VRAM pressure can limit throughput on high-resolution inputs
- –Limited controls for color management and HDR-to-SDR handling
- –Motion-heavy scenes may show edge sharpening misalignment
Best for: Fits when solo editors need offline AI upscaling and denoising for compressed video sources.
HitPaw Video Enhancer
SMBAI video upscaling software for Windows and Mac.
Preview-to-render queue for enhancement iteration, with focus on artifact reduction before committing to full output.
HitPaw Video Enhancer targets offline video upscaling and artifact reduction with a focus on making upscaled footage look cleaner after compression. It supports resolution multiplier workflows for batch processing and lets users preview enhancement results before sending a full render queue.
The enhancer emphasizes spatial denoising and edge-aware sharpening-style outputs to reduce block artifacts and ringing. Motion quality remains dependent on source stability because the workflow centers on frame-by-frame restoration rather than full temporal consistency controls.
- +Batch processing workflow for large libraries of similar-resolution clips
- +Preview-first enhancement reduces wasted renders on low-quality sources
- +Spatial denoising output can soften compression noise without heavy blur
- +Resolution multiplier controls fit common upscaling needs for SDR exports
- –Temporal flicker can appear on footage with rapid scene changes
- –Limited control over codec handling reduces predictability for complex pipelines
- –No explicit interframe alignment controls for difficult motion
- –Requires strong GPU resources to keep inference latency reasonable
Best for: Fits when a local, offline batch upscaling workflow is needed for compressed footage, not for heavy temporal stabilization.
Media.io Video Enhancer
SMBOnline AI video quality enhancer and upscaler.
Preview-driven enhancement for compression artifact mitigation, reducing blockiness while keeping fine edges readable.
Media.io Video Enhancer focuses on AI upscaling for everyday video files without exposing model controls, and it emphasizes artifact reduction around edges and compression noise. It processes uploads into higher-resolution outputs using a resolution-multiplier workflow and generates a final render that can be reviewed via a preview step.
The tool’s core capability is inference-only enhancement of existing clips, including frame-by-frame restoration for higher perceived sharpness. Batch processing is available for multiple assets, but it stays oriented around file conversion rather than a fully configurable render pipeline.
- +Quick workflow for single files and small batches with clear output artifacts
- +Good edge-focused sharpening without obvious global over-smoothing
- +Preview render step helps judge enhancement before committing to final outputs
- +Handles common consumer video inputs and codec outputs for offline use
- –Limited control over temporal consistency outcomes across fast motion scenes
- –Less transparent inference latency behavior for large or high bitrate sources
- –Few knobs for output encoding settings and bitrate preservation strategy
- –Works best for enhancement, not for HDR upscaling or advanced color pipeline control
Best for: Fits when creators need faster AI upscaling for offline clips with minimal workflow configuration.
Vmake AI
SMBAI video upscaling and enhancement platform.
Batch processing pipeline optimized for consistent upscale settings across a render queue.
Vmake AI focuses on AI upscaling for video files with a workflow geared toward offline processing rather than real-time output. It provides an image-restoration style enhancement pass that targets sharper edges and reduced compression artifacts while preserving original framing.
The workflow supports batch processing so multiple clips can be queued and rendered with consistent settings across a library. Output handling is oriented around practical codec and container compatibility for moving from source footage to an export queue.
- +Batch queue lets teams process many clips with consistent upscale settings
- +Artifact reduction improves readability of text and fine textures in motion
- +Footage-focused pipeline targets better edge definition than simple resize
- +Offline render workflow fits cloud or workstation GPU throughput planning
- –Temporal flicker can appear on low-light or highly detailed scenes
- –Upscale settings offer limited control over noise floor estimation
- –Frame alignment errors may show around fast motion and scene cuts
- –High-resolution outputs increase VRAM requirements and inference latency
Best for: Fits when creators and post teams need consistent offline AI upscaling across batches for sharper exports.
Fotor Video Enhancer
SMBOnline AI video enhancement tool.
One-click enhancement that combines upscaling with compression artifact reduction for rapid turnaround exports.
Fotor Video Enhancer applies AI-based upscaling and artifact reduction to uploaded video files, then exports an enhanced render for viewing or editing. It focuses on automated restoration rather than manual controls, with a workflow designed around selecting a source, running enhancement, and downloading the output.
The tool aims to improve perceived sharpness and reduce common compression damage by re-rendering frames at a higher resolution. It supports batch-like usage through repeated runs, which suits light pipelines but does not target studio render farm operations.
- +Upload-to-enhance workflow reduces setup overhead for quick restorations
- +Automated artifact mitigation helps hide compression damage in many clips
- +Straightforward export output supports immediate downstream editing
- +Works well for single-source upscaling without tuning parameters
- –Limited control over frame interpolation and motion handling accuracy
- –No documented batch processing pipeline with queue management
- –Preview and final render quality controls are not detailed for precision workflows
- –Temporal flicker risk can increase on low-light or highly compressed footage
Best for: Fits when small teams need fast AI upscaling outputs with minimal parameter tuning for offline edits.
Clideo Video Enhancer
SMBOnline video enhancement and editing tools.
Cloud-based one-click enhancement that prioritizes fast turnaround over codec-level and frame-level export control.
Clideo Video Enhancer targets editors who need quick AI upscaling without standing up a GPU rendering farm. The workflow focuses on uploading a video, applying enhancement, and downloading an upgraded output for offline use.
Upscaling is handled in the cloud, so output quality depends on source compression level and the service’s internal restoration model. Batch processing and advanced output controls are limited compared with desktop upscalers that expose codec, frame-rate, and export options.
- +Cloud upscaling removes local GPU and driver setup
- +Simple upload to enhanced output workflow fits ad hoc editing
- +Works for common consumer formats without manual frame handling
- +Predictable results for moderate resolution and artifact levels
- –Limited control over output codec, bitrate, and container choices
- –No exposed automation hooks for CI pipelines or scheduled batch jobs
- –Temporal flicker can appear on clips with heavy motion or cuts
- –Detail hallucination risk increases on low-bitrate, noisy sources
Best for: Fits when small teams need quick sharper exports from existing videos without pipeline engineering.
Conclusion
After evaluating 10 art design, TensorPix 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.
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 ai upscaling video software
Teams using AI upscaling video software typically care about temporal flicker behavior, batch throughput, and how predictably the output artifacts change across a render queue. This guide covers TensorPix, Cutout Pro, Aiseesoft Video Enhancer, Pixop, AVCLabs Video Enhancer AI, HitPaw Video Enhancer, Media.io Video Enhancer, Vmake AI, Fotor Video Enhancer, and Clideo Video Enhancer.
The tool set spans offline render queue utilities, preview-to-render workflows, and upload-based enhancers that trade control for speed. The buying sections focus on what the pipeline exposes for motion-heavy footage and how each workflow handles sharpness, denoise tuning, and artifact reduction consistency.
AI upscaling video software for sharper exports with controlled temporal stability
AI upscaling video software applies AI restoration models to raise resolution while managing compression artifacts through spatial denoise, edge-aware sharpening, and artifact reduction settings. The practical difference between products shows up in how they preserve interframe coherence and whether temporal flicker can be tuned down for motion-heavy sequences.
TensorPix is built around temporal consistency tuning that targets frame-to-frame flicker for upscaled outputs and couples that with a batch render queue for series re-exports. Cutout Pro pairs a cutout-first restoration workflow with batch upscaling that keeps settings consistent across long queues, but temporal flicker can still appear on fast motion without careful settings.
AI upscaling video quality controls, throughput, and workflow control
Teams buying ai upscaling video software usually judge output by whether temporal flicker stays controlled across motion-heavy sequences and whether sharpness gains stay stable across an offline render queue. For many workflows, the practical difference comes from how each product exposes temporal stability tuning, batch pipeline behavior, and artifact mitigation knobs that change ringing, block artifacts, and edge halos.
Temporal consistency tuning versus limited motion stabilization
TensorPix targets frame-to-frame flicker by offering temporal consistency tuning that reduces temporal flicker on motion-heavy footage, while still pairing it with batch rendering. Aiseesoft Video Enhancer and Pixop have limited temporal consistency controls for high motion sequences, so flicker can persist without additional care.
Batch render queue design for repeatable series exports
TensorPix and Pixop are built around offline render queue workflows that support multi-clip processing with consistent output management. Cutout Pro and Vmake AI also emphasize batch queues that keep upscale settings consistent across longer runs, which helps reduce operator-to-operator variation.
Preview-to-render iteration depth for fast artifact correction
HitPaw Video Enhancer uses a preview-to-render queue so teams can iterate on enhancement choices before committing to full output, which can reduce wasted renders. Media.io Video Enhancer also leads with preview-driven enhancement for compression artifact mitigation, which favors quicker single-file results.
Artifact mitigation controls for denoise, sharpen, and compression damage
Aiseesoft Video Enhancer couples upscaling with denoise and sharpening tuning and then exports a codec-selected final file, which makes spatial artifact reduction a more explicit part of the workflow. Pixop and AVCLabs Video Enhancer AI focus on artifact reduction that helps limit ringing and block artifacts during offline batch upscaling.
Resource and scaling limits that affect maximum frame size and throughput
Cutout Pro and AVCLabs Video Enhancer AI report VRAM limits that restrict maximum frame size per run and can cap throughput on high-resolution inputs. Vmake AI and TensorPix also face motion-specific edge stability and detail control constraints, which show up as temporal flicker behavior in certain scenes.
Choose based on motion control needs and the shape of the pipeline
The first fork is whether the output must stay temporally stable across motion-heavy sequences, because some products expose temporal flicker reduction as a tunable capability while others treat temporal consistency as a weak point. The second fork is whether the workflow is a repeatable offline render queue or an upload-first or preview-first iteration loop, because queue design changes how predictable output stays across many clips.
Prioritize temporal flicker reduction when footage has fast motion
Select TensorPix when motion-heavy footage shows frame-to-frame flicker because its temporal consistency tuning is specifically positioned to reduce perceived stability issues in upscaled outputs. Avoid relying on tools like Pixop or Aiseesoft Video Enhancer when high motion sequences need strong temporal stability because their temporal consistency controls are limited.
Use a batch render queue when the delivery is a multi-clip series
Choose Cutout Pro or Pixop when long queues must use consistent settings across many compressed videos, because their batch video upscaling and per-job output management fit re-encode workflows. Pick TensorPix when the same series also needs temporal flicker reduction, since its batch render queue is paired with temporal tuning.
Run preview-to-render iteration when wasted renders are the main risk
Choose HitPaw Video Enhancer when artifact reduction choices require iteration because its preview-first enhancement workflow helps reduce wasted full renders. Choose Media.io Video Enhancer for quick preview-driven single-file improvements when the workflow must stay light on parameter management.
Match VRAM and frame-size ceilings to the largest inputs in the library
Select Cutout Pro or AVCLabs Video Enhancer AI only if available GPU memory fits the maximum frame size, because both report VRAM limits or pressure that can restrict throughput. If large high-resolution clips repeatedly hit memory ceilings, prefer queue tools that keep runs consistent with careful parameter selection, because over-sized frames increase the chance of instability.
Confirm codec and export control needs before choosing upload-based tools
Choose Aiseesoft Video Enhancer when the export path depends on codec-selected final files, because its enhancement workflow ends with codec-selected output. Choose Clideo Video Enhancer only when ad hoc uploads and fast turnaround matter most, because codec-level, bitrate, and container controls are limited and there are no exposed automation hooks for CI pipelines or scheduled jobs.
Who benefits from these AI upscaling video software workflows
Buyers with production pipelines usually need consistent settings across batches and predictable artifact behavior after re-encoding, not just higher resolution previews. Buyers with creator workflows often prioritize speed, minimal configuration, and quick iteration loops that reach acceptable sharpness without heavy tuning.
Post-production teams exporting many clips with consistent settings
TensorPix, Pixop, and Cutout Pro fit series re-exports because batch render queue behavior supports multi-clip workflows with controlled output management.
Editors working with motion-heavy footage that shows temporal flicker
TensorPix is the clearest match because it focuses on reducing frame-to-frame flicker through temporal consistency tuning, while Pixop and Aiseesoft Video Enhancer have limited temporal consistency controls.
Solo editors needing offline upscaling with spatial artifact cleanup
AVCLabs Video Enhancer AI and HitPaw Video Enhancer support offline batch enhancement, with HitPaw adding a preview-to-render queue that reduces wasted iterations.
Creators optimizing for fast single-file improvements
Media.io Video Enhancer and Fotor Video Enhancer emphasize preview or one-click workflows that help mitigate compression damage quickly when deep motion control is not the main requirement.
Small teams that prefer upload-based processing over local GPU setup
Clideo Video Enhancer and Fotor Video Enhancer support upload-to-enhance and cloud upscaling workflows, but they limit codec, bitrate, and container control compared with offline queue tools.
Common pitfalls when buying and operating AI upscaling video software
A common mistake is choosing based only on still-frame sharpness, then discovering that temporal flicker appears on motion-heavy scenes after running a full batch. Another mistake is assuming all tools share the same batch and export control depth, because some tools are queue-focused while others are upload-first or preview-first and expose fewer knobs for complex pipelines.
Assuming temporal stability is automatic across fast motion footage
Temporal flicker can still appear in Pixop, Cutout Pro, and Aiseesoft Video Enhancer on fast motion when settings are not tuned for stability. TensorPix is specifically designed around temporal consistency tuning for motion-heavy outputs.
Overlooking VRAM constraints that cap maximum frame size and reduce throughput
Cutout Pro and AVCLabs Video Enhancer AI can hit VRAM limits that restrict maximum frame size per run. Running the largest clips without testing can lead to slow throughput or reduced processing scope.
Choosing upload-based tools for pipelines that require codec, bitrate, and container control
Clideo Video Enhancer limits output codec, bitrate, and container choices and does not expose automation hooks for CI pipelines. Offline queue tools like Pixop and TensorPix fit workflows that need consistent handoff outputs.
Skipping preview iteration when artifacts are likely on compressed or already sharp sources
Aiseesoft Video Enhancer can over-sharpen already crisp sources, and temporal artifacts can persist on high motion footage. HitPaw Video Enhancer’s preview-to-render queue helps validate artifact behavior before committing to full output.
How We Selected and Ranked These Tools
We evaluated TensorPix, Cutout Pro, Aiseesoft Video Enhancer, Pixop, AVCLabs Video Enhancer AI, HitPaw Video Enhancer, Media.io Video Enhancer, Vmake AI, Fotor Video Enhancer, and Clideo Video Enhancer by weighting features at 40 percent, ease at 30 percent, and value at 30 percent. Features scoring centered on how each tool handles temporal flicker behavior on motion-heavy footage, how its batch render queue supports multi-clip processing, and how its artifact reduction and sharpening controls affect ringing, block artifacts, and edge behavior.
Ease scoring tracked whether the workflow supports repeatable settings across batches or relies on upload or preview steps that trade control for speed. TensorPix ranked highest because it pairs a batch render queue with temporal consistency tuning that directly targets frame-to-frame flicker, while still delivering strong overall ease and features.
Frequently Asked Questions About ai upscaling video software
Which tool fits teams that need temporal consistency tuning to reduce temporal flicker?
How do offline render queue workflows differ between Pixop and Vmake AI?
What breaks if a video contains heavy compression artifacts and the workflow lacks artifact reduction controls?
Which tool is best when the pipeline needs codec-selected exports for re-encode workflows?
How do Cutout Pro and AVCLabs Video Enhancer AI differ in what they optimize during enhancement?
When is a preview step more useful than sending a full queue to render?
How do local workstation pipelines compare with cloud rendering for upscaling control?
Which tool supports deeper operational control for repeatable job outputs across many clips?
What security and access controls should be validated when using cloud-based upscaling?
Tools reviewed
Primary sources checked during evaluation.
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