{"id":"W1680549972","doi":"10.48550/arxiv.1206.5240","title":"Analysis of Semi-Supervised Learning with the Yarowsky Algorithm","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Algorithm","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":[0.0001836816,0.0001870902,0.0003022736,0.0001861148,0.0001851281,0.0000588676,0.001504694,0.0001084597,0.00002640753],"category_scores_gemma":[0.000002828377,0.0001415403,0.0002286587,0.001995562,0.000108,0.0001711217,0.001007283,0.00049346,0.00001103701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000377359,"about_ca_system_score_gemma":0.00005252837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001206545,"about_ca_topic_score_gemma":0.00003511387,"domain_scores_codex":[0.9988776,0.0001117242,0.0001248337,0.0005456257,0.00009471332,0.0002455398],"domain_scores_gemma":[0.9983584,0.0001352554,0.0002502436,0.001035731,0.0001256339,0.00009468966],"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":[0.000004858035,0.00005759762,0.006445231,0.00001211315,0.0007256188,0.00001316258,0.0003022021,0.958272,0.00002814657,0.02840077,0.0001070397,0.005631214],"study_design_scores_gemma":[0.0001080434,0.00001745319,0.004005013,0.00001539102,0.0007432089,8.884629e-7,0.00007135006,0.9933541,0.00004300355,0.0005569395,0.0008888043,0.0001958254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1425687,0.00006630315,0.8559261,0.0001886605,0.00005430521,0.0001590799,0.000006916744,0.0000882397,0.000941688],"genre_scores_gemma":[0.9967173,0.00008572148,0.002121052,0.00005691837,0.00004912461,0.000001658875,0.00001776979,0.000008745804,0.0009417047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8541486,"threshold_uncertainty_score":0.5771844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04503086522525263,"score_gpt":0.1800891524977699,"score_spread":0.1350582872725172,"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."}}