C-Rank Grade: D (Risky) - 8/100
External PR Merge Rate: -
Response Time: -
First Timer Success: -
robertsdionne/neural-network-papers has a recorded first-timer success rate of 0.0%. Repositories ranked D typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in robertsdionne/neural-network-papers respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 8.0 places robertsdionne/neural-network-papers in the D tier.
The external contributor pull request merge rate for robertsdionne/neural-network-papers is 0.0%, based on public PR activity from non-core contributors.
robertsdionne/neural-network-papers does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Open-source repository analyzed with precision C-Rank telemetry.
Collaborative maintainer environment in robertsdionne/neural-network-papers. Reviews community pull requests with focus on project quality.
Discussions in robertsdionne/neural-network-papers focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/robertsdionne/neural-network-papers?utm_source=github&utm_medium=badge)
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (81% approval). Large PRs (>500 lines) take ~115 hours (29% approval). Keep your diff compact for rapid review.
No cached good first issues currently tracked for robertsdionne/neural-network-papers.
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When evaluating whether to contribute to robertsdionne/neural-network-papers, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of robertsdionne/neural-network-papers acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 0.0% were successfully merged into the primary branch.
robertsdionne/neural-network-papers has a recorded first-timer success rate of 0.0%. Repositories ranked Risky typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 8.0 places robertsdionne/neural-network-papers in the Risky tier.
The external pull request merge rate is 0.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
robertsdionne/neural-network-papers does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for robertsdionne/neural-network-papers are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.