{"id":"W4250340144","doi":"10.1145/3297280.3329355","title":"Session details: Theme: AI and agents: BIO - Bioinformatics track","year":2019,"lang":"en","type":"article","venue":"","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Session (web analytics); Computer science; Theme (computing); Track (disk drive); Data science; Bioinformatics; World Wide Web; Biology","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":[0.0003101507,0.0001770566,0.0001641333,0.00007525432,0.00007091946,0.00008368718,0.0002416164,0.0002339661,0.0003317501],"category_scores_gemma":[0.000105986,0.0001257778,0.00007110875,0.00009840236,0.0001575335,0.00001369475,0.0002786325,0.0001355264,0.0003500025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001004894,"about_ca_system_score_gemma":0.00008317614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008995421,"about_ca_topic_score_gemma":0.00001077634,"domain_scores_codex":[0.9986955,0.00002650023,0.0003491086,0.0002183032,0.0003279453,0.0003826664],"domain_scores_gemma":[0.9991801,0.00001536322,0.00006866187,0.0003922335,0.0001089962,0.000234588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003153005,0.0004799961,0.0430427,0.001302194,0.0003351057,0.000006930296,0.001067249,0.00002538926,0.3583813,0.0006268318,0.1412354,0.4531816],"study_design_scores_gemma":[0.003671573,0.002145034,0.01354315,0.000124973,0.00004878714,0.0000662877,0.002736278,0.03400845,0.3072641,0.0004467301,0.6348214,0.001123236],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683643,0.0003790261,0.005338439,0.0009633625,0.0002996832,0.0005218989,0.00002609192,0.00003287741,0.0240743],"genre_scores_gemma":[0.9808508,0.001687502,0.005379361,0.002766323,0.0001405969,0.000009008817,0.0001939862,0.00002220752,0.008950226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4935859,"threshold_uncertainty_score":0.5129068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685256094284784,"score_gpt":0.291772497292655,"score_spread":0.2749199363498072,"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."}}