{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001211196,0.0002594859,0.0002386659,0.0001681833,0.0001098508,0.00007440263,0.0002420368,0.0001225198,0.0002451224],"category_scores_gemma":[0.0000128107,0.0002463372,0.00003585301,0.0003168202,0.00001551151,0.0002416888,0.00003753461,0.0002893807,0.00001734404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003689678,"about_ca_system_score_gemma":0.0002677482,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4882762,"about_ca_topic_score_gemma":0.9099682,"domain_scores_codex":[0.9984444,0.00002803259,0.0004141947,0.0002792917,0.0002926491,0.000541424],"domain_scores_gemma":[0.9992788,0.00001988032,0.00007544106,0.0002926008,0.00007611973,0.0002571197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004642671,0.0001474282,0.2475191,0.0001061748,0.00004032682,0.0001321761,0.0004348934,0.02920436,0.07453169,0.0001327397,0.01440416,0.6333005],"study_design_scores_gemma":[0.0005888616,0.00005785074,0.8645041,0.0001907294,0.000006902472,0.00001895779,0.00004840938,0.1014839,0.02486761,0.0001031459,0.007626012,0.0005035806],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936169,0.00004275396,0.00002480626,0.0006964776,0.0006107292,0.00007112058,0.00003833839,0.0001157304,0.004783138],"genre_scores_gemma":[0.9984447,0.0001493137,0.0002938207,0.00006439573,0.0003643174,0.00000943943,0.0001158947,0.00003711408,0.0005210262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6327969,"threshold_uncertainty_score":0.9999989,"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."}}