{"id":"W7020734135","doi":"","title":"Nature's Past Episode 050: Canadian Energy History","year":2015,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Energy consumption; Per capita; Energy (signal processing); Historiography; Consumption (sociology); Unit (ring theory); Natural history; Politics; Political history","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004984882,0.0007171883,0.0006253149,0.003015072,0.000290723,0.0003107999,0.005070888,0.001252062,0.001197939],"category_scores_gemma":[0.00002476352,0.0009599107,0.0002979024,0.001953762,0.0003677927,0.003454014,0.001845788,0.001245648,0.0004136467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890242,"about_ca_system_score_gemma":0.002592964,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01980714,"about_ca_topic_score_gemma":0.01463229,"domain_scores_codex":[0.9968708,0.0002220337,0.0001763726,0.001271678,0.0005544131,0.0009046713],"domain_scores_gemma":[0.9968969,0.0001005621,0.0003872678,0.001293109,0.00005878573,0.001263407],"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":[0.00002953734,0.00004045249,0.0006417644,0.00002128844,0.0001313444,0.002006946,0.00008835396,0.0001236597,1.400871e-7,0.1091306,0.8865762,0.001209759],"study_design_scores_gemma":[0.0006270412,0.00005810595,0.00002477985,0.0001403632,0.000051306,0.00002022749,0.0002903173,0.0003502778,7.558115e-7,0.0002163333,0.9972046,0.001015912],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000005748464,0.0008640823,0.01120078,0.0005464567,0.001529787,0.0001702656,0.0006077439,0.001825941,0.9832492],"genre_scores_gemma":[0.002059505,0.00008739976,0.004559413,0.0004726341,0.000498071,8.423286e-8,0.0009038614,0.0003049883,0.991114],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1106284,"threshold_uncertainty_score":0.9997151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007323512262133897,"score_gpt":0.1504699828630758,"score_spread":0.1431464706009419,"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."}}