{"id":"W4404736300","doi":"10.1080/03081079.2024.2430349","title":"SCNet: semi-supervised and contrastive learning against noisy labels with two selection strategy approach","year":2024,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Selection (genetic algorithm); Computer science; Artificial intelligence; Machine learning; Natural language processing","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":[0.004861125,0.001327406,0.001530919,0.001130518,0.0007279262,0.00120807,0.003740643,0.002012066,0.00206286],"category_scores_gemma":[0.009642828,0.000536143,0.0008328251,0.0008514322,0.001579064,0.002082361,0.002423323,0.002006547,0.000695535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001356906,"about_ca_system_score_gemma":0.002139326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002792692,"about_ca_topic_score_gemma":0.003689781,"domain_scores_codex":[0.9981489,0.0007672792,0.00006697293,0.000374751,0.0005022347,0.0001398734],"domain_scores_gemma":[0.995583,0.002220405,0.0002934274,0.000635345,0.001032149,0.0002357089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001162037,0.0004672619,0.003503934,0.0001565824,0.0002109594,0.0002816828,0.0001315937,0.6607973,0.009108515,0.0260158,0.008450666,0.2897137],"study_design_scores_gemma":[0.00002131452,0.00004226813,0.00006083031,0.000003142214,0.00000544184,0.000015285,0.00000318334,0.9954926,0.0009930256,0.003150765,0.0002087911,0.000003429079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02329776,0.0002167763,0.9727917,0.0003330113,0.00006569518,0.0001343204,0.0001038389,0.00166981,0.001387179],"genre_scores_gemma":[0.6085349,0.0001466333,0.3834583,0.000768344,0.000154606,0.0004894513,0.0007471053,0.0003377824,0.0053629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004861125,"threshold_uncertainty_score":0.02570844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322299775126577,"score_gpt":0.2686158366759177,"score_spread":0.2553928389246519,"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."}}