{"id":"W4408010102","doi":"10.1016/j.knosys.2025.113210","title":"Fine-grained local label correlation for multi-label classification","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Multi-label classification; Correlation; Off-label use; Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence; Mathematics; Medicine; Internal medicine; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005600012,0.0002293388,0.0002772467,0.0004580099,0.0002975386,0.0002686136,0.0009347181,0.0002550314,0.000002775966],"category_scores_gemma":[0.000282048,0.0002147265,0.00008095125,0.001043792,0.0001171972,0.0003219503,0.00009053884,0.0001502796,0.0001634845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002761818,"about_ca_system_score_gemma":0.000323358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001597276,"about_ca_topic_score_gemma":0.00004829978,"domain_scores_codex":[0.9982172,0.0001188744,0.0005628847,0.0005976175,0.0001809143,0.0003225162],"domain_scores_gemma":[0.9979205,0.0003871663,0.0002483511,0.000903839,0.0004783055,0.00006185207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003099747,0.0006037296,0.001293812,0.0003087381,0.0000386213,6.108983e-7,0.0001616256,0.0002766756,0.004214459,0.7596793,0.02167702,0.2117144],"study_design_scores_gemma":[0.002449086,0.00008384498,0.0008974397,0.0001292123,0.00001712006,5.808851e-7,0.0001310241,0.9677938,0.002299375,0.0009775819,0.02500166,0.0002192962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005326039,0.001104897,0.9903663,0.00173768,0.001955501,0.001122827,0.00001205354,0.001220462,0.001947658],"genre_scores_gemma":[0.9470995,0.000003522001,0.04237262,0.00006616995,0.00005895579,0.0009503115,0.00006684523,0.00001684483,0.00936521],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9675171,"threshold_uncertainty_score":0.8756292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07526925042946161,"score_gpt":0.3345912497241709,"score_spread":0.2593219992947093,"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."}}