{"id":"W1985833509","doi":"10.1089/cmb.2010.0123","title":"Finding Nearly Optimal GDT Scores","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computation; Conjecture; Heuristic; Mathematics; Algorithm; Statistics; Combinatorics; Set (abstract data type); Computer science; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003464307,0.001513827,0.002340673,0.004012408,0.00103314,0.002094256,0.001952019,0.001725532,0.003931486],"category_scores_gemma":[0.02013908,0.0007846286,0.00132894,0.002414695,0.001716735,0.002494933,0.002750021,0.001404314,0.001727701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481931,"about_ca_system_score_gemma":0.002581524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001862545,"about_ca_topic_score_gemma":0.002119304,"domain_scores_codex":[0.9960663,0.0007935733,0.000318563,0.001093882,0.001155516,0.0005722442],"domain_scores_gemma":[0.9946473,0.00292426,0.0004029278,0.0008571038,0.0008416959,0.0003266187],"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":[0.001462375,0.0004436662,0.04239128,0.0007933509,0.0004019215,0.001065275,0.0006072556,0.3083115,0.03130781,0.05520886,0.04327323,0.5147334],"study_design_scores_gemma":[0.0002515749,0.0002603496,0.004033369,0.00005748661,0.00008411055,0.0005940407,0.0002931444,0.8942481,0.01008699,0.08418539,0.00584005,0.00006544941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3560329,0.00138683,0.6221954,0.001064369,0.0001748636,0.0002196844,0.00237695,0.007480662,0.009068416],"genre_scores_gemma":[0.5531987,0.0002651087,0.438209,0.0003555275,0.00005600473,0.0001937586,0.005250833,0.0009265328,0.001544642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004012408,"threshold_uncertainty_score":0.01832128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0318057403196908,"score_gpt":0.2887815079475446,"score_spread":0.2569757676278538,"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."}}