{"id":"W4250950732","doi":"10.1163/9789004322714_cclc_2017-0028-035","title":"Putting a price on carbon pollution across Canada - Taking stock of progress, challenges, and opportunities as Canada prepares its national carbon pricing benchmark","year":2018,"lang":"en","type":"dataset","venue":"Climate Change and Law Collection","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Carbon stock; Stock (firearms); Benchmark (surveying); Carbon fibers; Environmental science; Economics; Natural resource economics; Business; Financial economics; Computer science; Geography; Ecology; Biology; Climate change; Cartography","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":[0.0009634156,0.001601645,0.001162531,0.004287781,0.001824533,0.003382586,0.003408353,0.002195108,0.01522826],"category_scores_gemma":[0.008144338,0.0005916021,0.001493804,0.00985247,0.0006547581,0.001360802,0.002167154,0.002377558,0.00951256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01321112,"about_ca_system_score_gemma":0.02015426,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9480852,"about_ca_topic_score_gemma":0.9770259,"domain_scores_codex":[0.9989527,0.00007714596,0.00006181732,0.000200934,0.0003762605,0.0003310353],"domain_scores_gemma":[0.9954981,0.0003656005,0.0003202777,0.0005511455,0.002679947,0.0005849956],"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":[0.00007915711,0.00003009521,0.008377363,0.0001630778,0.00005105277,0.0000225462,0.00004420584,0.0007030318,0.00004805794,0.0007903582,0.9871229,0.002568068],"study_design_scores_gemma":[0.0004308978,0.00002532857,0.09452847,0.0005872577,0.0001372995,0.00008945404,0.0007033059,0.00513576,0.0006159205,0.002516167,0.8951158,0.0001143859],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001992664,0.0001340444,0.00005599059,0.0003708801,0.00004176451,0.00001396435,0.9961225,0.0002077741,0.001060406],"genre_scores_gemma":[0.004919384,0.0001129625,0.0002589221,0.0001110007,0.00001431136,0.00004044746,0.9928091,0.0000464745,0.001687389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05191475,"threshold_uncertainty_score":0.1044409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1746499326781872,"score_gpt":0.2863723042964703,"score_spread":0.1117223716182831,"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."}}