{"id":"W7070416251","doi":"","title":"Ontario to Set Targets for Industries to Cut Carbon Emissions","year":2018,"lang":"en","type":"other","venue":"","topic":"Medicine, History, and Philosophy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Set (abstract data type); Carbon fibers; Air pollution; Production (economics)","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":[0.001878192,0.0005659644,0.0003483642,0.00167938,0.007041639,0.004251015,0.001267187,0.00470395,0.1300764],"category_scores_gemma":[0.008100773,0.0004358102,0.0008100726,0.001990833,0.001090402,0.001592391,0.002216361,0.002988859,0.01701104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06305633,"about_ca_system_score_gemma":0.2072898,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9592722,"about_ca_topic_score_gemma":0.9841765,"domain_scores_codex":[0.9970372,0.0001446223,0.00006489173,0.0001005338,0.001671381,0.0009814263],"domain_scores_gemma":[0.9920094,0.0004492656,0.0001948328,0.0001992172,0.005090861,0.002056455],"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":[0.00004468549,0.00003857851,0.001638644,0.00009224192,0.000006706657,0.00005355043,0.0002056111,0.0001127636,0.0001201934,0.01685788,0.9675145,0.01331466],"study_design_scores_gemma":[0.00004529305,0.00001520167,0.008245051,0.0001025427,0.000009603616,0.00002534863,0.0005663523,0.0001747363,0.0002971271,0.001833532,0.9886709,0.00001433052],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005792367,0.001028292,0.0007477494,0.04955947,0.001485729,0.0004297479,0.01896183,0.0003607365,0.9216341],"genre_scores_gemma":[0.02225884,0.0009848999,0.001264448,0.007805499,0.0001738234,0.0001941471,0.003970133,0.0002166211,0.9631315],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1300764,"threshold_uncertainty_score":0.4575078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05354870919590804,"score_gpt":0.3054877548286937,"score_spread":0.2519390456327857,"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."}}