{"id":"W4394241884","doi":"10.6084/m9.figshare.3408073","title":"Data from: Fast robust SUR with economical and actuarial applications","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Actuarial science; Business","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":[0.003162302,0.003038683,0.0015255,0.003383256,0.0007289355,0.001791949,0.004651878,0.00335287,0.01008217],"category_scores_gemma":[0.01067065,0.0007974295,0.002529002,0.00335868,0.0008431721,0.00148737,0.002549599,0.002020036,0.01349101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477394,"about_ca_system_score_gemma":0.001608288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716214,"about_ca_topic_score_gemma":0.02620598,"domain_scores_codex":[0.9981561,0.0004084422,0.0002182709,0.0005092894,0.0005295656,0.0001783314],"domain_scores_gemma":[0.9967672,0.0008871392,0.000306241,0.00133815,0.0005534714,0.0001478421],"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":[0.0009245796,0.000491417,0.01351903,0.001815583,0.0005182486,0.0003365099,0.00008046447,0.0441828,0.0009410017,0.002635713,0.8717136,0.06284114],"study_design_scores_gemma":[0.002134148,0.0005217644,0.03325719,0.0005731811,0.000266628,0.001103704,0.0002372345,0.135542,0.008020915,0.01707119,0.8009672,0.000304802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01480611,0.001024485,0.007491626,0.0008575335,0.0003241275,0.0003046749,0.9664547,0.006666054,0.002070571],"genre_scores_gemma":[0.01750716,0.0002122287,0.01207674,0.000167455,0.00005148268,0.0004129538,0.9681363,0.0002000105,0.00123561],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01716214,"threshold_uncertainty_score":0.03412449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03804013198828585,"score_gpt":0.2225416240025774,"score_spread":0.1845014920142915,"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."}}