{"id":"W4252292706","doi":"10.22215/etd/2011-06845","title":"Targeted optimizatino of computational and classification performance of a protein-protein interaction predictor","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Humanities; Art","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001033546,0.0001598221,0.0001871632,0.00009256761,0.00003025204,0.000007140047,0.0001127412,0.0002512526,0.00008737136],"category_scores_gemma":[0.00008484315,0.0001511758,0.00004705453,0.00006168582,0.00004821827,0.000008639481,0.0000280972,0.000136668,0.000002076864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006653064,"about_ca_system_score_gemma":0.00009357971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001747447,"about_ca_topic_score_gemma":0.000009047579,"domain_scores_codex":[0.9990753,0.00003489926,0.0004727964,0.0001758076,0.0001548209,0.00008638069],"domain_scores_gemma":[0.9987836,0.000005787649,0.000699823,0.0001770657,0.0003025057,0.00003122513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001995611,0.000291815,0.005337899,0.005683712,0.0003562832,2.121794e-7,0.001187603,0.005173202,0.9646374,0.001493478,0.0003214415,0.01352136],"study_design_scores_gemma":[0.001059797,0.001872381,0.05372142,0.0007220173,0.00008128789,0.000007612733,0.0005677075,0.1753923,0.7648811,0.0001275535,0.001013079,0.000553733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894627,0.00007893813,0.0020511,0.000004147835,0.00006799003,0.0005035456,0.00002857995,0.00001099052,0.007791976],"genre_scores_gemma":[0.9617825,0.00003554196,0.02944381,0.000005421822,0.00002905931,0.00004575363,0.003909849,0.00002171824,0.004726333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1997563,"threshold_uncertainty_score":0.6164772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009266706375626686,"score_gpt":0.2488603973627018,"score_spread":0.2395936909870751,"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."}}