{"id":"W3152460829","doi":"10.3368/le.032421-0031r","title":"Adaptation to Natural Disasters through the Agricultural Land Rental Market: Evidence from Bangladesh","year":2022,"lang":"en","type":"article","venue":"Land Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; Grantham Foundation for the Protection of the Environment; Centre for Climate Change Economics and Policy, University of Leeds; International Development Research Centre; Department for International Development; Government of the United Kingdom","keywords":"Renting; Natural disaster; Agriculture; Agricultural land; Business; Natural resource economics; Agricultural economics; Economics; Geography","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":[0.0005582093,0.0001885203,0.0002438686,0.000543948,0.0003978707,0.0006292429,0.0003529773,0.0004135051,0.00327588],"category_scores_gemma":[0.003337088,0.0001613494,0.0002203039,0.001515441,0.0009253029,0.0006621513,0.0005435963,0.0004057873,0.0004620976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007959357,"about_ca_system_score_gemma":0.0004915646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06372584,"about_ca_topic_score_gemma":0.08063175,"domain_scores_codex":[0.9996547,0.0001461853,0.00002564173,0.00004432743,0.00005668764,0.00007252195],"domain_scores_gemma":[0.9958771,0.001593617,0.001715683,0.0002366876,0.0003643486,0.0002125287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003081725,0.0002415542,0.9681492,0.0001762205,0.000181724,0.001112416,0.002659658,0.003618949,0.00168628,0.001675153,0.001218149,0.01897248],"study_design_scores_gemma":[0.00002631533,0.0001791662,0.9877672,0.00003842872,0.00006826576,0.0002378997,0.006059278,0.001997266,0.0005251931,0.0007294838,0.002342733,0.00002878843],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956499,0.000176442,0.0001479837,0.0002975882,0.000001414568,0.00001208367,0.0004521752,0.000003868491,0.003258597],"genre_scores_gemma":[0.9990767,0.0003566252,0.00005379472,0.00002502395,0.000002202728,0.000003975884,0.0001796305,0.000001160355,0.0003007909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06372584,"threshold_uncertainty_score":0.1267098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800671345404499,"score_gpt":0.1987609513823263,"score_spread":0.1807542379282813,"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."}}