{"id":"W3174963945","doi":"","title":"Can we assess Greenhouse Gas Emission trends in Canada's largest population center?","year":2018,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Center (category theory); Population; Environmental science; Geography; Geology; Environmental health; Medicine","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":[0.00116344,0.0005916379,0.0004016353,0.003670981,0.002129828,0.003468505,0.001492134,0.0007181991,0.003718111],"category_scores_gemma":[0.007069072,0.0002276911,0.0007082307,0.009289772,0.0007131154,0.00227039,0.001005453,0.001202948,0.0005785393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03867523,"about_ca_system_score_gemma":0.07203089,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.996933,"about_ca_topic_score_gemma":0.9987147,"domain_scores_codex":[0.9989342,0.00007874166,0.00004953106,0.0001186277,0.0003483189,0.0004706153],"domain_scores_gemma":[0.9943188,0.0003063625,0.000467814,0.00008529428,0.004107313,0.0007143387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008548902,0.00007388431,0.9264308,0.0001928526,0.0002447336,0.00007657787,0.0009874417,0.003380658,0.0001951001,0.00403638,0.03065121,0.033645],"study_design_scores_gemma":[0.00001149453,0.00003318411,0.9592001,0.000256568,0.000130315,0.00003164566,0.00684548,0.003894401,0.0004349389,0.001061661,0.02805587,0.00004425862],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7512092,0.008534342,0.0035031,0.03827966,0.0004992712,0.0002206772,0.1165568,0.0002747934,0.0809221],"genre_scores_gemma":[0.9695357,0.004181598,0.002279483,0.002444957,0.00007053554,0.00007343978,0.01501331,0.00005129828,0.00634968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03867523,"threshold_uncertainty_score":0.2806098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635182121186508,"score_gpt":0.2514384805021126,"score_spread":0.2250866592902476,"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."}}