{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005732595,0.001630027,0.001930423,0.001392428,0.004490264,0.01101273,0.001810473,0.006158321,0.4212885],"category_scores_gemma":[0.004538214,0.0006421544,0.00143413,0.002149672,0.0006204199,0.006084537,0.007036716,0.005763448,0.2543144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003232922,"about_ca_system_score_gemma":0.006416002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004095732,"about_ca_topic_score_gemma":0.01006782,"domain_scores_codex":[0.9981681,0.0002950986,0.00007371113,0.0004772194,0.0005388359,0.0004470578],"domain_scores_gemma":[0.9929048,0.0005551723,0.000173599,0.0003627816,0.002032302,0.003971231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009667311,0.0000745629,0.0001564845,0.0001200994,0.000006745052,0.00001384547,0.00003017614,0.00007956308,0.0003005366,0.001381586,0.9740487,0.02369091],"study_design_scores_gemma":[0.00006114998,0.0001038418,0.0008284567,0.0001458757,0.00001085968,0.00004186064,0.00009265188,0.0005852019,0.0004268226,0.002428924,0.9952552,0.00001921252],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004564068,0.01779548,0.02288535,0.1328949,0.2173835,0.003608483,0.02186275,0.006299464,0.572706],"genre_scores_gemma":[0.01416636,0.00628477,0.005959296,0.009193963,0.04327514,0.001265464,0.01255643,0.001766718,0.9055319],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5787115,"threshold_uncertainty_score":0.8254617,"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."}}