{"id":"W4255329564","doi":"10.2172/1408734","title":"Idaho National Laboratory Quarterly Occurrence Analysis 3rd Quarter FY2017","year":2017,"lang":"en","type":"report","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Fiscal year; Business; Geography; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002397443,0.0007887949,0.0004168138,0.007914986,0.001474449,0.00330509,0.001045872,0.0004602772,0.05341914],"category_scores_gemma":[0.008696951,0.0003608498,0.0003470301,0.00520428,0.0002424977,0.0008691189,0.001243362,0.0008362497,0.03374561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004950806,"about_ca_system_score_gemma":0.0102928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1847481,"about_ca_topic_score_gemma":0.1510988,"domain_scores_codex":[0.9963951,0.0001624311,0.0001979343,0.0001958437,0.002824337,0.0002244852],"domain_scores_gemma":[0.9877081,0.0006821596,0.001347835,0.0005285511,0.009317243,0.0004161405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000056153,0.00007497759,0.008718394,0.0001139388,0.00001178363,0.00003278457,0.00006039559,0.0001958945,0.0001330262,0.000779386,0.9725177,0.01730564],"study_design_scores_gemma":[0.00003263939,0.00004903712,0.06736581,0.0003133299,0.00002565988,0.00005079882,0.0008481501,0.000837261,0.001362106,0.0005020232,0.9285843,0.00002875945],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01582074,0.0006571868,0.001703179,0.003610767,0.00196954,0.0009909971,0.7176802,0.001703059,0.2558644],"genre_scores_gemma":[0.03778993,0.00278767,0.004500569,0.001542685,0.0008465571,0.00160621,0.6852237,0.0007550392,0.2649476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1847481,"threshold_uncertainty_score":0.3673456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2422878708417878,"score_gpt":0.4749029998459188,"score_spread":0.2326151290041309,"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."}}