{"id":"W6920616206","doi":"10.6084/m9.figshare.22240476.v1","title":"Multi-level climate governance: examining impacts and interactions between national and sub-national emissions mitigation policy mixes in Canada","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Policy Transfer and Learning","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Climate policy; Context (archaeology); Policy analysis; Subsidy; Greenhouse gas; Climate change; Public policy; Policy studies","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":[0.0001646427,0.00006828351,0.0000771214,0.00010477,0.000380495,0.00005947276,0.00006228078,0.00003950386,0.001548796],"category_scores_gemma":[0.003229298,0.00007514667,0.00001118836,0.0004019173,0.00001250079,0.0002769454,0.00003252015,0.0001416562,0.00003722227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004425292,"about_ca_system_score_gemma":0.001884832,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3529153,"about_ca_topic_score_gemma":0.8073845,"domain_scores_codex":[0.9990808,0.00006996436,0.0001295205,0.0001384318,0.000333609,0.000247642],"domain_scores_gemma":[0.9992236,0.0004919704,0.00004337274,0.00002465657,0.00008458848,0.0001318122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002506793,0.00006750241,0.614401,0.0005541608,0.0001140654,0.00003644805,0.05797446,0.0005730309,0.00253862,0.004474037,0.2732509,0.04599077],"study_design_scores_gemma":[0.0001711622,0.000002932736,0.9861107,0.0004655344,0.000001241541,8.151875e-7,0.001006028,0.0004720005,0.00004076903,0.00007778228,0.01156561,0.00008539848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7780969,0.0001407887,0.000001157611,0.004909122,0.00006378822,0.0002294959,0.2077852,0.00008512965,0.008688407],"genre_scores_gemma":[0.9930285,0.00006510916,0.00003523731,0.0001481232,0.0001607463,0.00002948312,0.006396383,0.000007086387,0.0001293495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4544692,"threshold_uncertainty_score":0.9993639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1733300842986144,"score_gpt":0.3907717923799169,"score_spread":0.2174417080813026,"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."}}