{"id":"W4213346984","doi":"10.2172/1473629","title":"Idaho National Laboratory Quarterly Occurrence Analysis 4th Quarter FY 2017","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; National laboratory; Engineering; Business; Geography; Finance; 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":[0.003178574,0.0008390094,0.0004724334,0.008933284,0.001641045,0.003868327,0.001100139,0.0004819319,0.06506575],"category_scores_gemma":[0.01257898,0.0004398872,0.0003724601,0.006224486,0.000299195,0.001047699,0.001409653,0.001027006,0.04230293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005013133,"about_ca_system_score_gemma":0.01230134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1439263,"about_ca_topic_score_gemma":0.1112708,"domain_scores_codex":[0.9945527,0.0002553814,0.0002927116,0.0002662248,0.004335275,0.0002977744],"domain_scores_gemma":[0.980907,0.00131576,0.002093786,0.0009299123,0.01414749,0.0006059313],"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":[0.0000493654,0.00008399779,0.007876518,0.0001065241,0.00001000838,0.00002714961,0.00006336134,0.0001812557,0.0001171009,0.001055755,0.9726212,0.01780777],"study_design_scores_gemma":[0.0000341665,0.00004994871,0.05435841,0.0003408853,0.00002306263,0.00005369396,0.0008314368,0.0008710296,0.001321714,0.0005994872,0.9414853,0.00003090907],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01468784,0.0006446532,0.002547213,0.004973296,0.002263735,0.001266474,0.6693659,0.002238835,0.3020121],"genre_scores_gemma":[0.03720735,0.003403484,0.006826015,0.001772198,0.001041119,0.002211091,0.6615756,0.000997387,0.2849658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1439263,"threshold_uncertainty_score":0.2861772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2415323565562023,"score_gpt":0.4695139644675407,"score_spread":0.2279816079113384,"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."}}