{"id":"W4404788214","doi":"10.1109/tetci.2024.3502453","title":"Impact of Strategic Sampling and Supervision Policies on Semi-Supervised Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Educational and Psychological Assessments","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Sampling (signal processing); Business; Policy learning; Process management; Knowledge management; Psychology; Computer science; Machine learning","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":[0.0272482,0.002152273,0.002538189,0.0008780947,0.001688398,0.002073808,0.00378214,0.003532222,0.001606242],"category_scores_gemma":[0.1074033,0.0009120791,0.000777592,0.0008843918,0.004779652,0.007253597,0.004452704,0.004405133,0.000621567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00266277,"about_ca_system_score_gemma":0.004983021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00533452,"about_ca_topic_score_gemma":0.006111208,"domain_scores_codex":[0.9841411,0.01124584,0.0005128047,0.001940368,0.001563654,0.0005963194],"domain_scores_gemma":[0.8756635,0.102049,0.004013639,0.01080056,0.004755178,0.002718119],"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":[0.002192128,0.000846194,0.01112676,0.0004378402,0.0002573427,0.0002154707,0.0006868338,0.7641845,0.00238916,0.06493966,0.006681369,0.1460428],"study_design_scores_gemma":[0.0001026987,0.000168396,0.0003868081,0.00004257708,0.00002133113,0.00005092851,0.00006166918,0.9629676,0.001136714,0.03461969,0.0004243504,0.00001723008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1375635,0.004076935,0.8450367,0.004047721,0.0002432247,0.0004275758,0.000241137,0.001894196,0.00646897],"genre_scores_gemma":[0.8853374,0.0008938059,0.1094359,0.001451655,0.0002517266,0.0003434374,0.0004216608,0.0002480325,0.001616346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0272482,"threshold_uncertainty_score":0.144104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.161760600672919,"score_gpt":0.4614509155077224,"score_spread":0.2996903148348034,"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."}}