{"id":"W4251793657","doi":"10.1163/9789004322714_cclc_2016-0133-003","title":"Ontario Climate Policy Through a Climate Justice Lens","year":2018,"lang":"en","type":"dataset","venue":"Climate Change and Law Collection","topic":"Social Sciences and Governance","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Climate justice; Lens (geology); Economic Justice; Geography; Climate change; Political science; Environmental science; Ecology; Law; Engineering; Biology","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":[0.001680624,0.000877689,0.000751831,0.004223323,0.001868674,0.002696933,0.001760176,0.001285184,0.03434393],"category_scores_gemma":[0.01373158,0.0006042133,0.0009024118,0.01021724,0.0006967235,0.001162557,0.002061453,0.00138647,0.01376748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01627124,"about_ca_system_score_gemma":0.0289383,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8968633,"about_ca_topic_score_gemma":0.9596469,"domain_scores_codex":[0.9983497,0.000231108,0.0001240626,0.0002636815,0.0006823405,0.0003492046],"domain_scores_gemma":[0.9935995,0.001053922,0.0006085031,0.001089021,0.003056964,0.0005920671],"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":[0.00003402452,0.00001203951,0.004639332,0.0001705734,0.00002329963,0.00001458807,0.000083107,0.0003037603,0.00002794943,0.001711065,0.9907064,0.002273786],"study_design_scores_gemma":[0.0000830034,0.000004537724,0.02817363,0.0002304395,0.00003310536,0.00001914451,0.0002225047,0.0007102339,0.0001505575,0.001300342,0.9690507,0.00002178371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001193166,0.0001187073,0.0001399193,0.0004379223,0.00002657942,0.0000308723,0.9932228,0.0001378569,0.004692175],"genre_scores_gemma":[0.005874603,0.0002126146,0.000648008,0.000149955,0.00002044199,0.000242682,0.9850681,0.00007482076,0.00770882],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1031367,"threshold_uncertainty_score":0.2074882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147871619390362,"score_gpt":0.362203594189171,"score_spread":0.2474164322501349,"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."}}