{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/evaluate-classifier","entry":"evaluate_classifier","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":5,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":7,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":2,"unverified":2},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2608.08321","paper":"/paper/arxiv-2608-08321","title":"Spatial Heterogeneity-Aware Multi-Hazard Susceptibility and Risk Mapping at Regional Scale","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"GVCL/Susceptibility-Mapping-FL-Hetero","path":"src/modeling/evaluation_metrics.py","file_url":"https://github.com/GVCL/Susceptibility-Mapping-FL-Hetero/blob/HEAD/src/modeling/evaluation_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dab56270eb927ac6","mcp_get_code":{"code_sha256":"dab56270eb927ac6"}},{"arxiv_id":"2506.07883","paper":"/paper/diffusion-counterfactual-generation-with","title":"Diffusion Counterfactual Generation with Semantic Abduction","date":"2025-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajatrasal/diffusion-counterfactuals","path":"model/classifier.py","file_url":"https://github.com/rajatrasal/diffusion-counterfactuals/blob/HEAD/model/classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8afb07a694e85597","mcp_get_code":{"code_sha256":"8afb07a694e85597"}},{"arxiv_id":"2410.09343","paper":"/paper/elicit-llm-augmentation-via-external-in","title":"ELICIT: LLM Augmentation via External In-Context Capability","date":"2024-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lins-lab/elicit","path":"train_retriever.py","file_url":"https://github.com/lins-lab/elicit/blob/HEAD/train_retriever.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0d973e0a50d6364c","mcp_get_code":{"code_sha256":"0d973e0a50d6364c"}},{"arxiv_id":"2310.12565","paper":"/paper/open-world-lifelong-graph-learning","title":"Open-World Lifelong Graph Learning","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bobowner/open-world-lgl","path":"evaluation.py","file_url":"https://github.com/bobowner/open-world-lgl/blob/HEAD/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"34c45914408314d8","mcp_get_code":{"code_sha256":"34c45914408314d8"}},{"arxiv_id":"2307.09614","paper":"/paper/multi-view-self-supervised-learning-for","title":"Multi-view self-supervised learning for multivariate variable-channel time series","date":"2023-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theabrusch/multiview_ts_ssl","path":"src/multiview.py","file_url":"https://github.com/theabrusch/multiview_ts_ssl/blob/HEAD/src/multiview.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ba31ef2272c0583","mcp_get_code":{"code_sha256":"8ba31ef2272c0583"}},{"arxiv_id":"2305.09235","paper":"/paper/synthetic-data-real-errors-how-not-to-publish","title":"Synthetic data, real errors: how (not) to publish and use synthetic data","date":"2023-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bvanbreugel/deep_generative_ensemble","path":"DGE_utils.py","file_url":"https://github.com/bvanbreugel/deep_generative_ensemble/blob/HEAD/DGE_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a483d9850b21f9c","mcp_get_code":{"code_sha256":"4a483d9850b21f9c"}},{"arxiv_id":"2211.17046","paper":"/paper/raft-rationale-adaptor-for-few-shot-abusive","title":"Rationale-Guided Few-Shot Classification to Detect Abusive Language","date":"2022-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"punyajoy/rgfs_ecai","path":"Eval_code/eval_scripts.py","file_url":"https://github.com/punyajoy/rgfs_ecai/blob/HEAD/Eval_code/eval_scripts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"181bf586aa1c6ccc","mcp_get_code":{"code_sha256":"181bf586aa1c6ccc"}}]}