{"id":"W4382865832","doi":"10.1515/9780773554184-015","title":"Fiscal Outlier: Yukon in an Austere Age","year":2018,"lang":"en","type":"book-chapter","venue":"McGill-Queen's University Press eBooks","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Outlier; Medicine; Computer science; Artificial intelligence","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":[0.0007139382,0.000260918,0.0004615821,0.0007782847,0.005705285,0.009298883,0.0006469205,0.001680727,0.01431093],"category_scores_gemma":[0.002422488,0.0001579725,0.0001910624,0.003502676,0.002509127,0.00491898,0.003540945,0.003740955,0.002765718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00521996,"about_ca_system_score_gemma":0.01513585,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1492949,"about_ca_topic_score_gemma":0.2800275,"domain_scores_codex":[0.9995269,0.00004540494,0.00002422146,0.00004312683,0.000118506,0.0002418798],"domain_scores_gemma":[0.9994302,0.00005447458,0.00007395638,0.00003955007,0.0001866592,0.0002150113],"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":[0.0001773052,0.00003187079,0.0144609,0.0001078368,0.00001847039,0.0009838968,0.009833695,0.000270109,0.0000913363,0.2846422,0.6207982,0.06858407],"study_design_scores_gemma":[0.00001327195,0.00001576809,0.01380817,0.0002030151,0.000007624253,0.0003073686,0.02851236,0.0001278392,0.00007165948,0.02504615,0.9318641,0.00002269354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1587597,0.01978947,0.0007923503,0.2882421,0.0071181,0.0000200032,0.001772325,0.0003880283,0.5231179],"genre_scores_gemma":[0.6432254,0.01789337,0.0004876282,0.03246877,0.0009215213,0.00003365774,0.00136072,0.0005599946,0.3030488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8507051,"threshold_uncertainty_score":0.2968517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150475801715999,"score_gpt":0.1898691179142651,"score_spread":0.1748215377426652,"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."}}