{"id":"W2549065936","doi":"10.1371/journal.pcbi.1005128","title":"Ten Simple Rules for Developing Public Biological Databases","year":2016,"lang":"en","type":"editorial","venue":"PLoS Computational Biology","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute","keywords":"Biological database; Database; Computer science; Quality (philosophy); Set (abstract data type); Biological data; Data science; World Wide Web; Bioinformatics; 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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004219158,0.0003618777,0.0007030949,0.0006917844,0.000474042,0.0004793116,0.002350045,0.000380751,0.0002743257],"category_scores_gemma":[0.03719328,0.0002191177,0.000206861,0.000443936,0.000377041,0.0002097217,0.001630718,0.0002269596,0.0009525413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221735,"about_ca_system_score_gemma":0.0007076028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001264036,"about_ca_topic_score_gemma":0.00001728356,"domain_scores_codex":[0.994483,0.0004558282,0.00116831,0.001964059,0.001299802,0.0006289867],"domain_scores_gemma":[0.9602573,0.0362834,0.0006830076,0.0008870154,0.00176001,0.0001292689],"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":[0.00002416316,0.00004989812,0.0002988637,0.00001126222,0.0000822693,0.000001701565,0.000008081203,0.00002336767,0.000008964708,0.02453963,0.9385884,0.03636333],"study_design_scores_gemma":[0.000339832,0.00007095352,0.0001782928,0.00002612155,0.0000120023,8.677019e-7,0.00001797293,0.002738541,0.00000299537,0.1723321,0.8239872,0.0002930981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0006416405,0.0001433492,0.4622891,0.001956178,0.5213355,0.0004861227,0.01258023,0.0001710177,0.0003968537],"genre_scores_gemma":[0.005543084,0.00004578083,0.1059405,0.0006785232,0.8103766,0.0002472342,0.07555646,0.00005778933,0.001554021],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.3563486,"threshold_uncertainty_score":0.9998254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3304295872358272,"score_gpt":0.4498763318757231,"score_spread":0.1194467446398959,"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."}}