{"id":"W2803183763","doi":"10.3897/biss.2.25647","title":"Management of Molecular Data in DINA with SeqDB","year":2018,"lang":"en","type":"article","venue":"Biodiversity Information Science and Standards","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Workflow; Metadata; Computer science; Data science; Data management; Task (project management); Agile software development; Suite; Software; Set (abstract data type); World Wide Web; Engineering management; Database; Software engineering; Systems engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000604415,0.00004209897,0.00004699187,0.00005938863,0.00009800436,0.00002186018,0.0002183324,0.00001619466,0.000002356307],"category_scores_gemma":[0.0000202829,0.00003493358,0.000004854025,0.0001744817,0.0004337425,0.00001566347,0.0003849968,0.00001185492,0.000001536822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001667318,"about_ca_system_score_gemma":0.0001044413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001592336,"about_ca_topic_score_gemma":0.0000116652,"domain_scores_codex":[0.9993859,0.000003928769,0.00007511235,0.0001042703,0.000342622,0.00008819035],"domain_scores_gemma":[0.9993128,9.215217e-7,0.00003952292,0.0002103801,0.000410747,0.00002564224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002564014,0.0002397605,0.5135438,0.0006563311,0.0005090925,0.00002900838,0.0135467,0.0002083122,0.230159,0.004928893,0.02224385,0.2113712],"study_design_scores_gemma":[0.003422801,0.001778474,0.3613794,0.00006533374,0.00006182285,0.00001453531,0.01042737,0.0003228892,0.1650221,0.0000592448,0.4568629,0.0005831509],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945272,0.00005263295,0.0005830345,0.00006448977,0.00002494847,0.00007507335,0.0004163433,8.192321e-7,0.004255466],"genre_scores_gemma":[0.9989692,0.0001737836,0.0007559098,0.00007491528,0.000003052045,4.570562e-7,0.00001991677,3.123461e-7,0.000002433176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4346191,"threshold_uncertainty_score":0.1598142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144085814425997,"score_gpt":0.2533114538092071,"score_spread":0.2389028723666074,"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."}}