{"id":"W4292099683","doi":"10.1111/jiec.13322","title":"Using cumulative carbon budgets and corporate carbon disclosure to inform ambitious corporate emissions targets and long‐term mitigation pathways","year":2022,"lang":"en","type":"article","venue":"Journal of Industrial Ecology","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Overshoot (microwave communication); Greenhouse gas; Business; Global temperature; Environmental science; Carbon tax; Term (time); Carbon fibers; Consistency (knowledge bases); Climate change; Environmental economics; Natural resource economics; Global warming; Economics; Computer science; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007988246,0.0001772546,0.0006115944,0.0004640272,0.0002907371,0.00005677465,0.0001239374,0.0001920063,0.00009770933],"category_scores_gemma":[0.0002409631,0.00020309,0.00006836878,0.0002383216,0.00008393069,0.0001788743,0.0002132128,0.0005046924,0.000001785694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000409859,"about_ca_system_score_gemma":0.0001177489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002044005,"about_ca_topic_score_gemma":0.0002070952,"domain_scores_codex":[0.9983401,0.00005508468,0.00102088,0.0002310953,0.00004612184,0.000306708],"domain_scores_gemma":[0.9969964,0.0001015926,0.002482471,0.000114521,0.00006221316,0.0002427816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006100185,0.0001731053,0.9807609,0.00003173327,0.0002290786,0.0001632558,0.004975764,0.004296677,0.0007647677,0.00648755,0.0004679061,0.001039298],"study_design_scores_gemma":[0.02489663,0.009806715,0.6979994,0.0002647304,0.0003847032,0.002996072,0.005888688,0.05024586,0.001566543,0.1963284,0.006450799,0.003171548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956201,0.0003254935,0.0000120878,0.001782741,0.001137424,0.0002714922,0.0003038424,0.00000721434,0.0005396163],"genre_scores_gemma":[0.9988112,0.0001173617,0.00009010025,0.0004544403,0.0004213749,0.00001089865,0.0000214222,0.00002232385,0.00005090479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2827615,"threshold_uncertainty_score":0.828177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2327721118253616,"score_gpt":0.2850723589478415,"score_spread":0.05230024712247991,"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."}}