{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005902979,0.0003700667,0.0003091113,0.004008972,0.02005736,0.007159246,0.00126549,0.001394667,0.0311688],"category_scores_gemma":[0.001908876,0.0003504122,0.0003423881,0.0149332,0.003500127,0.002476609,0.002385405,0.00266079,0.00203744],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1462433,"about_ca_system_score_gemma":0.1038278,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9972618,"about_ca_topic_score_gemma":0.9990153,"domain_scores_codex":[0.9985691,0.00005030572,0.00002979961,0.0001333055,0.0006738316,0.0005435609],"domain_scores_gemma":[0.999047,0.00006256742,0.00005655763,0.00003517921,0.0005647848,0.000233894],"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":[0.00009573014,0.00003444274,0.01904614,0.0007395693,0.0000493377,0.001123214,0.04192683,0.0002871335,0.0003829359,0.1996663,0.642846,0.09380241],"study_design_scores_gemma":[0.000001730313,0.000001878542,0.01558968,0.0001199368,0.000006878789,0.0000809298,0.0064567,0.00002484565,0.00004395141,0.001004659,0.9766487,0.00002008302],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.05544588,0.02520884,0.0003302773,0.0369072,0.001547564,0.00006166418,0.02303421,0.0001171473,0.8573472],"genre_scores_gemma":[0.5459119,0.04101551,0.001073174,0.0160138,0.0003649287,0.00009264096,0.01410966,0.000302245,0.3811161],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1462433,"threshold_uncertainty_score":0.9902368,"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."}}