{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008806746,0.0002174766,0.0002563881,0.00003475,0.00007085392,0.00001860337,0.0002954876,0.0003273304,0.001128126],"category_scores_gemma":[0.00004530425,0.00009305568,0.00005915058,0.0001723582,0.00004223675,0.00001927478,0.00002502044,0.0001769283,0.00001701154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006456781,"about_ca_system_score_gemma":0.0001322028,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1474021,"about_ca_topic_score_gemma":0.3236357,"domain_scores_codex":[0.998924,0.00001349455,0.0001864919,0.0003547412,0.0001266482,0.0003945924],"domain_scores_gemma":[0.9994505,0.00008893071,0.0000958654,0.00008100629,0.00001850666,0.0002651774],"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":[0.000006687651,0.00004189131,0.002245758,0.00008220663,0.00001842193,0.000006411089,0.00008840505,3.580888e-7,0.004291905,0.0002370404,0.9912881,0.001692817],"study_design_scores_gemma":[0.00008480263,0.00007384717,0.002043736,0.0002143777,0.00001001635,0.000005844887,0.0006314325,0.00002037909,0.0005960857,0.0001829141,0.9958402,0.0002963906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02897744,0.000415225,5.339888e-7,0.001654663,0.0001706683,0.0009153139,0.004494146,0.0001039905,0.963268],"genre_scores_gemma":[0.312598,0.00005108108,0.0002145512,0.0001695312,0.001175764,0.0002143923,0.002017895,0.00001734525,0.6835414],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2836206,"threshold_uncertainty_score":0.999785,"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."}}