{"id":"W4391246662","doi":"10.1088/1748-9326/ad231b","title":"Causal analysis of Canada’s environment-growth nexus for inclusive development metrics","year":2024,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nexus (standard); Greenhouse gas; Gross domestic product; Economics; Human Development Index; Sustainable development; Index (typography); Natural resource economics; Measures of national income and output; Public economics; Eco-efficiency; Human development (humanity); Economic growth; Development economics; Macroeconomics; Political science; Ecology","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":[0.003698459,0.0004621778,0.0006645914,0.004372565,0.001961338,0.002833323,0.0008205192,0.0006044351,0.01796172],"category_scores_gemma":[0.02573308,0.0002410905,0.001130594,0.004962695,0.001539574,0.001427872,0.001979656,0.001471328,0.0003057824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02122911,"about_ca_system_score_gemma":0.03755009,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9388127,"about_ca_topic_score_gemma":0.9299338,"domain_scores_codex":[0.9982633,0.0005061,0.00009126847,0.0002757318,0.0004923504,0.0003713258],"domain_scores_gemma":[0.9804438,0.009385279,0.002430662,0.0008328778,0.00591368,0.0009935654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001447556,0.00008641401,0.5076885,0.0004007509,0.0006772311,0.0006886336,0.001327344,0.0393432,0.0003167562,0.3758964,0.02089833,0.0525316],"study_design_scores_gemma":[0.00006319672,0.00008153101,0.4836966,0.0007457092,0.001013073,0.0002742178,0.005511109,0.1922827,0.001014419,0.2400761,0.07509022,0.0001510339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.727169,0.007048917,0.05859194,0.02935159,0.0004059883,0.0007409737,0.02425817,0.000639646,0.151794],"genre_scores_gemma":[0.9871429,0.0009053949,0.004506189,0.00031979,0.00003272782,0.00006900916,0.001576891,0.00002870952,0.005418402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06118727,"threshold_uncertainty_score":0.1540287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03333578806721058,"score_gpt":0.2517884733736246,"score_spread":0.2184526853064141,"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."}}