{"id":"W3033862284","doi":"10.1109/icmla51294.2020.00180","title":"Learning across label confidence distributions using Filtered Transfer Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Mitacs","keywords":"Transfer of learning; Computer science; Machine learning; Artificial intelligence; Task (project management); Artificial neural network; Deep learning; Retraining; Range (aeronautics); Low Confidence; Meta learning (computer science)","routes":{"ca_aff":true,"ca_fund":true,"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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006583382,0.0004103704,0.000448507,0.00007543872,0.0008024247,0.001191383,0.001716571,0.0003182156,0.00008207765],"category_scores_gemma":[0.0006256605,0.0004183452,0.0001623794,0.0004413198,0.00009906638,0.0004422611,0.001753222,0.002996631,0.0001493827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001178751,"about_ca_system_score_gemma":0.0002614168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006610728,"about_ca_topic_score_gemma":0.00002902886,"domain_scores_codex":[0.9966092,0.0005022586,0.000548465,0.001300655,0.0004764241,0.0005630075],"domain_scores_gemma":[0.9982485,0.000218793,0.0002063677,0.0008898737,0.0001993485,0.0002371257],"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":[0.00008795279,0.000477319,0.01439377,0.001331634,0.0004905394,0.0001784831,0.02247365,0.2947109,0.05354613,0.4139281,0.001951599,0.19643],"study_design_scores_gemma":[0.0003281697,0.00008019251,0.002227233,0.0001514837,0.00002721077,0.00001945751,0.000211092,0.9802923,0.0007410547,0.001431505,0.01390695,0.0005833114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02604179,0.0001075188,0.9672968,0.003626567,0.0004079222,0.0002381222,0.00006426658,0.001164591,0.001052427],"genre_scores_gemma":[0.9539472,0.00007127562,0.04390114,0.0001949707,0.0001647165,0.0000290773,0.0007727316,0.00003192166,0.000886914],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9279054,"threshold_uncertainty_score":0.9998455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08604629868095026,"score_gpt":0.3459375196527729,"score_spread":0.2598912209718227,"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."}}