{"id":"W4389115139","doi":"10.48550/arxiv.2311.14686","title":"Causal Models Applied to the Patterns of Human Migration due to Climate Change","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Climate change; Context (archaeology); Corporate governance; Affect (linguistics); Bayesian network; Risk analysis (engineering); Business; Political science; Computer science; Sociology; Geography; Artificial intelligence; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002727378,0.0005736448,0.0004815714,0.001975078,0.0006997779,0.001483807,0.001430609,0.001610939,0.004156836],"category_scores_gemma":[0.01338836,0.0005113137,0.001091411,0.001911436,0.001176512,0.001970137,0.001397383,0.001784991,0.0002807916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001692005,"about_ca_system_score_gemma":0.001112727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02682185,"about_ca_topic_score_gemma":0.02008875,"domain_scores_codex":[0.9992724,0.0003946398,0.00003839366,0.0001389675,0.00008117767,0.00007452061],"domain_scores_gemma":[0.9931906,0.005267074,0.0007763408,0.0002450441,0.0003209058,0.0002000793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002021494,0.00002522391,0.004651711,0.00003340775,0.00006150576,0.00008892969,0.0001211908,0.8941888,0.0001141211,0.09159778,0.001053017,0.00804408],"study_design_scores_gemma":[0.000003530693,0.000003475056,0.0004156408,0.000006420405,0.000007064191,0.000009840317,0.00002409015,0.9375226,0.00002952908,0.06144038,0.0005327874,0.00000461857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07661828,0.0007350672,0.9113452,0.003860427,0.0002260122,0.0000518048,0.001093161,0.0003828767,0.005687176],"genre_scores_gemma":[0.940343,0.001397234,0.05046679,0.0002708302,0.0002945845,0.0001533587,0.0009532468,0.00009278624,0.006028148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02682185,"threshold_uncertainty_score":0.05333143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3737707670464828,"score_gpt":0.2768284080720113,"score_spread":0.09694235897447145,"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."}}