{"id":"W1514717448","doi":"10.3233/jem-2010-0336","title":"Socioeconomic patterns in climate data","year":2010,"lang":"en","type":"article","venue":"Journal of Economic and Social Measurement","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada; University of Alabama","keywords":"Spatial analysis; Climate change; Urbanization; Socioeconomic status; Spatial econometrics; Covariate; Econometrics; Geography; Climatology; Counterfactual thinking; Spatial ecology; Data set; Physical geography; Environmental science; Statistics; Mathematics; Economics; Ecology; Demography; Geology; Sociology; Population; Economic growth","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004558779,0.0004194943,0.0005278972,0.004779561,0.0006495626,0.001637856,0.00106214,0.0006258875,0.01093545],"category_scores_gemma":[0.03428212,0.000385811,0.000534581,0.0183406,0.0004634382,0.0009618216,0.001842319,0.0009854593,0.005132672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002051204,"about_ca_system_score_gemma":0.002303194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07279269,"about_ca_topic_score_gemma":0.07398851,"domain_scores_codex":[0.993398,0.001796748,0.001253014,0.0008617651,0.002290994,0.0003995319],"domain_scores_gemma":[0.9805844,0.005992814,0.003003766,0.005176878,0.004606804,0.0006354098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003317274,0.0002278499,0.7630613,0.0008833267,0.0004510929,0.0002813952,0.001901367,0.01380919,0.001070905,0.0144706,0.1204818,0.08302951],"study_design_scores_gemma":[0.00005455416,0.00007875481,0.7680995,0.0001547796,0.00007120728,0.0001992617,0.001161517,0.005432569,0.001131936,0.003993765,0.2195495,0.00007256521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09017116,0.0003277306,0.00777754,0.0006797906,0.0001062938,0.0006389603,0.8754889,0.000347697,0.024462],"genre_scores_gemma":[0.2321519,0.0004850563,0.01426305,0.000217549,0.000057785,0.001369313,0.7457219,0.0001605681,0.005572836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07279269,"threshold_uncertainty_score":0.144738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04281721581217516,"score_gpt":0.251307635848662,"score_spread":0.2084904200364869,"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."}}