{"id":"W2093324069","doi":"10.1111/j.1467-9361.2009.00538.x","title":"Smoothing Income against Crop Flood Losses in Amazonia: Rain Forest or Rivers as a Safety Net?","year":2010,"lang":"en","type":"article","venue":"Review of Development Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Amazon rainforest; Economics; Fishing; Flood myth; Consumption smoothing; Agricultural economics; Asset (computer security); Natural resource economics; Geography; Fishery; Unemployment; Ecology; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000462653,0.0001934524,0.0004284792,0.00001703118,0.0001403757,0.00003137073,0.0004224127,0.0001030857,0.0001994197],"category_scores_gemma":[0.0001503072,0.00006669343,0.00009468873,0.0002612666,0.00008006599,0.0001934621,0.0001459506,0.0002072418,0.00007091797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007236617,"about_ca_system_score_gemma":0.0001048345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001178596,"about_ca_topic_score_gemma":0.006251414,"domain_scores_codex":[0.9986197,0.0000374083,0.0006665282,0.0003005231,0.00009299225,0.0002828524],"domain_scores_gemma":[0.9993505,0.0001502127,0.0002707645,0.00007211053,0.00004926591,0.0001071496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000778011,0.0001925196,0.1911041,0.001268916,0.00005154747,0.00002251974,0.000373576,0.0002236525,0.005162871,0.0009291541,0.0003240385,0.8002694],"study_design_scores_gemma":[0.0003075568,0.00008449312,0.7886038,0.002621355,0.00001274804,0.00001715565,0.0001593031,0.00003861795,0.001444176,0.0001595728,0.2060688,0.0004823319],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955983,0.001823032,7.516991e-7,0.0008022951,0.0001531004,0.0003930172,0.000009227165,0.00001962538,0.001200723],"genre_scores_gemma":[0.8955793,0.1004079,0.002713045,0.0007899278,0.00008019392,0.00002454525,0.0000667922,0.000001925249,0.0003363197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.799787,"threshold_uncertainty_score":0.3488436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009312958607943447,"score_gpt":0.2186737447141417,"score_spread":0.2093607861061982,"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."}}