{"id":"W6980615549","doi":"","title":"Clearing the air: How carbon pricing helps Canada fight climate change","year":2018,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Plant chemical constituents analysis","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Clearing; Climate change; Greenhouse gas; Carbon fibers; Global warming","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004987164,0.0003257771,0.0003270314,0.00002394177,0.001550915,0.0000938461,0.0007458032,0.0001596474,0.0001794861],"category_scores_gemma":[0.0002986206,0.0001274053,0.0001517903,0.0007144137,0.0001280999,0.0003292138,0.0004061163,0.0004904757,0.00004094936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003391036,"about_ca_system_score_gemma":0.0000120571,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1719356,"about_ca_topic_score_gemma":0.6884181,"domain_scores_codex":[0.9974983,0.0001677527,0.0003019615,0.0006184733,0.0005944374,0.0008190978],"domain_scores_gemma":[0.9988617,0.0002991475,0.0001975003,0.0002093419,0.0001673861,0.0002648671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005161573,0.00006629232,0.003413799,0.00002498962,0.0001241248,0.00007325994,0.000005785167,0.000002389737,0.8473457,0.006782728,0.00003799332,0.1420714],"study_design_scores_gemma":[0.0005070401,0.0001877779,0.03656883,0.0003167063,0.0003131433,0.00008242895,0.0004907138,0.0003574514,0.3461738,0.001052449,0.6121885,0.001761202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780381,0.00007506563,6.800427e-9,0.00137919,0.000287904,0.0002430287,0.0004935594,0.000127518,0.01935559],"genre_scores_gemma":[0.9974878,0.00006768796,0.00002622762,0.001852498,0.0002588282,0.00003040361,0.00004891368,0.000005071522,0.0002225058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6121505,"threshold_uncertainty_score":0.9997489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310150845249072,"score_gpt":0.1903603916057534,"score_spread":0.1672588831532627,"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."}}