{"id":"W2508584866","doi":"10.2172/1260455","title":"Idaho National Laboratory Quarterly Occurrence Analysis - 1st Quarter FY 2016","year":2016,"lang":"en","type":"report","venue":"","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Quality (philosophy); National laboratory; Operations research; Engineering; Geography; Physics; Engineering physics","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":[0.002502438,0.0008037201,0.0004409691,0.005952344,0.001155271,0.002572545,0.001101855,0.0003543292,0.05017815],"category_scores_gemma":[0.00634724,0.0004342667,0.0003212806,0.003881428,0.0002610004,0.0009763927,0.0009951176,0.0007631432,0.02922891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004628263,"about_ca_system_score_gemma":0.009303334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1476737,"about_ca_topic_score_gemma":0.1442443,"domain_scores_codex":[0.9961403,0.0001781961,0.0002135998,0.0002184009,0.003077167,0.0001723419],"domain_scores_gemma":[0.9910833,0.0004660761,0.0008722683,0.0004831358,0.006836051,0.0002591683],"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":[0.000093531,0.0001777376,0.01426538,0.0001903799,0.00001486257,0.00003797618,0.00008598814,0.0002661311,0.0003787084,0.0009683768,0.9531192,0.03040165],"study_design_scores_gemma":[0.00003788837,0.00006344642,0.06950872,0.000311599,0.00002313229,0.00005862235,0.0006463559,0.000630566,0.002847547,0.0004334945,0.925409,0.00002961109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01519983,0.0005403023,0.002241817,0.002483572,0.001688661,0.001247965,0.7042666,0.001849823,0.2704814],"genre_scores_gemma":[0.03394908,0.002566697,0.007847923,0.001071824,0.0004855365,0.001587002,0.6458574,0.000822149,0.3058125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1476737,"threshold_uncertainty_score":0.2936283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009271653950179608,"score_gpt":0.2416443681407776,"score_spread":0.2323727141905979,"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."}}