{"id":"W7036961484","doi":"","title":"CSA Canadian Securities Administrators : China beats annual target for cutting carbon emissions in 2018","year":2019,"lang":"en","type":"other","venue":"","topic":"Phytochemistry Medicinal Plant Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; China; Emissions trading; Carbon fibers; Work (physics); Government (linguistics)","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":[0.002430043,0.0007569856,0.0004899008,0.001564343,0.005858661,0.007118028,0.001237604,0.008609534,0.07740466],"category_scores_gemma":[0.006894984,0.0003804243,0.0006540496,0.001975741,0.001288978,0.002198032,0.001697315,0.006162531,0.01472206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02632368,"about_ca_system_score_gemma":0.09763438,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6800789,"about_ca_topic_score_gemma":0.8054168,"domain_scores_codex":[0.9966817,0.0000807919,0.00004618656,0.0001457384,0.002185745,0.0008597592],"domain_scores_gemma":[0.9955895,0.0001752582,0.0001372683,0.0001245311,0.002746057,0.001227311],"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":[0.00002174218,0.00001208045,0.0004060868,0.00001067239,0.000002502263,0.00003884374,0.00002802261,0.00003708288,0.00009148633,0.009556876,0.9845671,0.00522754],"study_design_scores_gemma":[0.00001612619,0.000008425892,0.002414178,0.00002334242,0.000004937809,0.00001337855,0.00009299643,0.0001395785,0.0001622612,0.001076355,0.9960359,0.00001265479],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009547009,0.003353298,0.0006840691,0.278066,0.01659806,0.0002436905,0.008136257,0.001003989,0.6823676],"genre_scores_gemma":[0.02464431,0.001211273,0.0004446746,0.03388423,0.001593756,0.00006171023,0.001624504,0.0001535768,0.9363819],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6800789,"threshold_uncertainty_score":0.6436103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047038301812894,"score_gpt":0.2271731082756983,"score_spread":0.2167027252575694,"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."}}