{"id":"W4281747386","doi":"10.3390/math10111871","title":"Robust Multi-Label Classification with Enhanced Global and Local Label Correlation","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Correlation; Artificial intelligence; Subspace topology; Computer science; Pattern recognition (psychology); Missing data; Machine learning; Ambiguity; Feature learning; Mathematics","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":[0.0001697854,0.0001099736,0.0001162751,0.00005342129,0.0002623042,0.0001057391,0.0004257477,0.00004224855,0.00001548032],"category_scores_gemma":[0.00003032884,0.0000958262,0.0000112098,0.0004397355,0.00009103175,0.0002635026,0.0002635184,0.000121895,0.0000215319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001289585,"about_ca_system_score_gemma":0.00003879409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003178945,"about_ca_topic_score_gemma":0.000005817955,"domain_scores_codex":[0.9990696,0.00002753383,0.0002015811,0.0002654356,0.000286437,0.000149352],"domain_scores_gemma":[0.9992655,0.00005080088,0.000175589,0.000421281,0.00005170458,0.00003514402],"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":[0.000009388841,0.0006223306,0.0006517493,0.00004693585,0.00002100435,0.000002926592,0.001286316,0.0007756239,0.001187917,0.8256351,0.0006111456,0.1691495],"study_design_scores_gemma":[0.0007226747,0.0001291589,0.00126306,0.000009954865,0.000009559492,0.00002127505,0.001389391,0.9836189,0.0004053506,0.01203422,0.0002398143,0.0001566969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02180026,0.00005860966,0.9757035,0.0008888799,0.00007587729,0.0002153118,0.000004695537,0.0004244148,0.0008284894],"genre_scores_gemma":[0.6256431,0.000006802402,0.3738707,0.00005369769,0.000003825412,0.00007705433,0.000005887244,0.000005513221,0.0003333852],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9828432,"threshold_uncertainty_score":0.3907679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06129331094919142,"score_gpt":0.2703561215480288,"score_spread":0.2090628105988374,"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."}}