{"id":"W6930936221","doi":"10.5281/zenodo.14941604","title":"Type-Preserving Flat Closure Optimization Artifact","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artifact (error); Closure (psychology); Minification; Smoothing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005265026,0.00110333,0.0009217098,0.001315584,0.00092581,0.003740577,0.001663165,0.001144636,0.02053441],"category_scores_gemma":[0.01344198,0.000641347,0.001702387,0.001014464,0.001946708,0.002959025,0.00386873,0.002871733,0.009160581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022424,"about_ca_system_score_gemma":0.001866639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008815604,"about_ca_topic_score_gemma":0.0007789173,"domain_scores_codex":[0.9951717,0.001238306,0.0003918715,0.0008328532,0.002051036,0.0003141654],"domain_scores_gemma":[0.9938181,0.001593896,0.0003481156,0.003187357,0.0008312498,0.0002213131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002446387,0.00004776248,0.0003394247,0.0002355808,0.00006042138,0.0003082766,0.00009495424,0.005499315,0.006586761,0.808129,0.02890275,0.1495511],"study_design_scores_gemma":[0.00006559472,0.00005551185,0.000364556,0.00009141713,0.00005547543,0.0007740782,0.00002694747,0.05258305,0.02249425,0.8338993,0.08953744,0.00005238472],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.003524437,0.0003304806,0.9632452,0.0007220818,0.0007409834,0.00007225003,0.0006153948,0.002536841,0.02821232],"genre_scores_gemma":[0.2281154,0.0008479185,0.6741285,0.00234056,0.001156001,0.0003313095,0.001822975,0.009274899,0.08198249],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02053441,"threshold_uncertainty_score":0.06869441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3436648922461419,"score_gpt":0.4553259771469865,"score_spread":0.1116610849008445,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}