{"id":"W605329361","doi":"","title":"Weather Variability, Agriculture and Rural Migration: Evidence from State and District Level Migration in India","year":2014,"lang":"en","type":"preprint","venue":"OpenDocs (Institute of Development Studies)","topic":"Climate Change, Adaptation, Migration","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economy and Environment Program for Southeast Asia; United Nations University World Institute for Development Economics Research; International Development Research Centre; Direktoratet for Utviklingssamarbeid; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Agriculture; Yield (engineering); Geography; Productivity; Agricultural productivity; Linkage (software); Climate change; Internal migration; Census; Developing country; Population; Ecology; Economics; Biology; Demography; Economic growth","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":[0.0006565233,0.0002687628,0.0002591607,0.001224092,0.0007125356,0.001023067,0.0006565775,0.0003554369,0.001717303],"category_scores_gemma":[0.003221002,0.0002826837,0.0005830789,0.004870305,0.0008892065,0.0005665103,0.001414468,0.0006125137,0.0002876598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004108879,"about_ca_system_score_gemma":0.0006490716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09306809,"about_ca_topic_score_gemma":0.1152097,"domain_scores_codex":[0.999226,0.0003533062,0.00005093986,0.0001047281,0.00008393102,0.0001810161],"domain_scores_gemma":[0.9956546,0.001728011,0.001314389,0.0004187303,0.0003843753,0.0004999505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000569072,0.00004137877,0.996362,0.00001277878,0.0001299809,0.00008510769,0.001207288,0.0001858785,0.00008363437,0.000119552,0.0002048946,0.001510623],"study_design_scores_gemma":[0.000002721323,0.00002086826,0.9966531,0.000008177453,0.00005018952,0.00003165754,0.002794484,0.000126008,0.0000293316,0.00005197729,0.0002272914,0.000004219053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987722,0.000104825,0.00006009054,0.00009949331,0.000004947296,0.000002554289,0.0002847172,0.000002250993,0.0006688956],"genre_scores_gemma":[0.9992533,0.0001390788,0.00002930892,0.00001514972,0.000006587653,0.000003455275,0.0003154672,0.000001891014,0.0002357918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09306809,"threshold_uncertainty_score":0.1850528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.132364302425205,"score_gpt":0.3362721840608329,"score_spread":0.2039078816356279,"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."}}