{"id":"W1542499968","doi":"10.1007/978-3-642-13529-3_22","title":"Improving Co-training with Agreement-Based Sampling","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Co-training; Sampling (signal processing); Training (meteorology); Artificial intelligence; Machine learning; Training set; Process (computing); Data sampling; Pattern recognition (psychology); Data mining; Semi-supervised learning; Computer vision","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001255538,0.0006231255,0.0005370053,0.0008159925,0.0005520635,0.0009686893,0.003188924,0.0003209649,0.00004093209],"category_scores_gemma":[0.0001060875,0.0005138028,0.0001119902,0.0004883233,0.0006686486,0.0004305599,0.0005967653,0.00217118,0.00003885036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000149353,"about_ca_system_score_gemma":0.001048179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000672293,"about_ca_topic_score_gemma":0.0001017411,"domain_scores_codex":[0.9956588,0.00003245816,0.0004438367,0.001755332,0.001215859,0.0008937276],"domain_scores_gemma":[0.9971521,0.0005263829,0.0004180291,0.001458787,0.0002022137,0.0002424935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004877697,0.00001561782,0.0000979637,0.00003996969,0.000008172726,0.00008391134,0.0006461257,0.0413449,0.0009305554,0.002513569,0.000001286283,0.954313],"study_design_scores_gemma":[0.0005095842,0.000342295,0.00008360337,0.0004936919,0.000009827051,0.00008955579,3.496256e-7,0.9831012,0.002635853,0.009327758,0.002428783,0.000977531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001753905,0.00008546042,0.9945815,0.0005660178,0.001205537,0.0002801752,0.000003549803,0.0002879048,0.002814471],"genre_scores_gemma":[0.06515518,0.000002105954,0.9325106,0.001367091,0.0006541003,0.000008020193,0.000008508518,0.00004621891,0.0002482256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9533355,"threshold_uncertainty_score":0.9997314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073814148242,"score_gpt":0.2622375231549632,"score_spread":0.2414993816725432,"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."}}