{"id":"W3121789706","doi":"","title":"The Climate Change Learning Curve","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Climate change; Variance (accounting); Econometrics; Greenhouse gas; Shock (circulatory); Sensitivity (control systems); Function (biology); Global temperature; Order (exchange); Key (lock); Economics; Mathematics; Global warming; Computer science; Engineering; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002537589,0.0004275957,0.0006760706,0.0006075398,0.0009589654,0.003275516,0.0009318375,0.003563642,0.01219138],"category_scores_gemma":[0.04945976,0.0003045668,0.0006050438,0.0007640357,0.002273558,0.004423961,0.001870104,0.004417714,0.001767462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002602095,"about_ca_system_score_gemma":0.001415387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003372555,"about_ca_topic_score_gemma":0.001379092,"domain_scores_codex":[0.998929,0.000449546,0.00002932079,0.0002229865,0.0002288896,0.0001403443],"domain_scores_gemma":[0.9897691,0.007719194,0.0005627542,0.0006485036,0.0007137742,0.0005867154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001994025,0.00009046456,0.005332577,0.0001377264,0.00006458026,0.0002158079,0.0003398815,0.1197614,0.0005823178,0.7477752,0.02366778,0.1018329],"study_design_scores_gemma":[0.00002961975,0.00003968523,0.002980312,0.00005256084,0.00001490083,0.000124508,0.0001122171,0.1712219,0.0002331794,0.8057429,0.01941933,0.00002888759],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2255365,0.01107946,0.3498667,0.1407914,0.001524362,0.0001846864,0.002099775,0.001708271,0.2672089],"genre_scores_gemma":[0.9582754,0.00385739,0.01413972,0.002494444,0.0006462075,0.0001271982,0.0003942519,0.0001894541,0.0198759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219138,"threshold_uncertainty_score":0.04078424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1624742769587396,"score_gpt":0.3391502058427087,"score_spread":0.1766759288839691,"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."}}