{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001565249,0.000380975,0.0006522582,0.0001560055,0.0004733739,0.0001950179,0.0002874572,0.0002257145,0.00002800635],"category_scores_gemma":[0.0009394916,0.0003389537,0.00004689768,0.0003160207,0.0004182459,0.0007098998,0.0005862841,0.0002870077,0.000006828961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005979228,"about_ca_system_score_gemma":0.000632763,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01485186,"about_ca_topic_score_gemma":0.2791113,"domain_scores_codex":[0.9972179,0.0003033371,0.0008579645,0.000660903,0.0006506515,0.000309229],"domain_scores_gemma":[0.9982468,0.0003676547,0.0006304057,0.0002418683,0.0004062791,0.0001069387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001153538,0.000220393,0.5022599,0.0007989709,0.0004997018,0.00001656908,0.4625052,0.0005441118,0.0007833855,0.001717509,0.003445483,0.02709343],"study_design_scores_gemma":[0.000656713,0.00003133264,0.9615135,0.001976805,0.00009445398,0.000001245291,0.01321424,0.0001427235,0.0003860549,0.002207837,0.01910613,0.0006689977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909892,0.002489618,0.001293565,0.002056859,0.0008426962,0.001543051,0.0001174406,0.00003028839,0.0006372981],"genre_scores_gemma":[0.9681494,0.02169576,0.008532212,0.00006022807,0.0001674611,0.0002559465,0.0004409243,0.00001514299,0.0006829839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4592535,"threshold_uncertainty_score":0.9999062,"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."}}