{"id":"W2006338143","doi":"10.1371/journal.pcbi.1002487","title":"Rise and Demise of Bioinformatics? Promise and Progress","year":2012,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Demise; Field (mathematics); Data science; Meaning (existential); Key (lock); Biology; Computer science; Bioinformatics; Cognitive science; Epistemology; Psychology; Ecology; Political science; Philosophy","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":[0.0002533245,0.0001115232,0.0001550532,0.00006868264,0.00005166812,0.00001170089,0.00009777648,0.0001471773,0.00001029608],"category_scores_gemma":[0.0001973556,0.00008937456,0.00002786811,0.00005407951,0.0006147397,0.000008712207,0.0001968429,0.00006810522,0.000006182025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004573163,"about_ca_system_score_gemma":0.0000541123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000199608,"about_ca_topic_score_gemma":6.893125e-7,"domain_scores_codex":[0.9991503,0.00004556109,0.0002918003,0.0001233601,0.0001259582,0.00026304],"domain_scores_gemma":[0.9994038,0.00005427135,0.0001036505,0.0001037597,0.0001421662,0.0001923308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005554783,0.001128807,0.6401356,0.001680191,0.0006801472,0.000001538166,0.002187945,0.0000517429,0.1033114,0.004917252,0.002768029,0.2425818],"study_design_scores_gemma":[0.0124679,0.007993803,0.5382956,0.0003333046,0.0003476549,0.0004277642,0.001792266,0.1201853,0.2042397,0.01532683,0.09580114,0.002788693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943081,0.002915693,0.001779878,0.0002472979,0.00005448858,0.0002512416,0.00005266595,0.000008185399,0.0003824635],"genre_scores_gemma":[0.976281,0.0003349586,0.02297669,0.00009283079,0.0001040551,0.0000156499,0.0001531239,0.000007101547,0.00003461457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2397931,"threshold_uncertainty_score":0.3644589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075483066391383,"score_gpt":0.2920245276201478,"score_spread":0.271269696956234,"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."}}