{"id":"W2792881081","doi":"10.1080/17565529.2018.1442807","title":"Climate change adaptations of shrimp farmers: a case study from southwest coastal Bangladesh","year":2018,"lang":"en","type":"article","venue":"Climate and Development","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Khulna University; International Development Research Centre; Department for International Development; Ministry of Environment and Forests; Department for International Development, UK Government; Government of the United Kingdom","keywords":"Shrimp farming; Shrimp; Sustainability; Fishery; Climate change; Context (archaeology); Agriculture; Aquaculture; Netting; Geography; Business; Ecology; Biology; Fish <Actinopterygii>","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":[],"consensus_categories":[],"category_scores_codex":[0.0001477283,0.0001365494,0.0001855545,0.00002849106,0.0003349444,0.00002020805,0.00006272062,0.00002427563,0.0003763251],"category_scores_gemma":[0.000005833647,0.0001118902,0.00001865142,0.00009903753,0.00011373,0.0001171336,0.0006490391,0.000037399,0.0002235445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002821156,"about_ca_system_score_gemma":0.000007233659,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003786303,"about_ca_topic_score_gemma":0.0273292,"domain_scores_codex":[0.9990357,0.0000234087,0.0002727251,0.0002637958,0.0001437309,0.0002606699],"domain_scores_gemma":[0.9996693,0.00002854518,0.00008943205,0.0001177928,0.00001791108,0.00007705855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002754347,0.0002294152,0.9102054,0.00001745806,0.00003288508,0.0001628391,0.02886995,1.892761e-7,0.0001577794,0.00001124469,0.0001085163,0.0601768],"study_design_scores_gemma":[0.0006096015,0.0002559621,0.9572176,0.00003289915,0.00002939024,0.00006115969,0.03621241,0.0000399749,0.0001405363,0.00001485906,0.005175837,0.0002097654],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996555,0.00004684505,0.000016098,0.00002475915,0.0001333649,0.0003501784,0.00007546342,0.00002885193,0.002769462],"genre_scores_gemma":[0.998565,0.0001024546,0.001085329,0.00005258176,0.00005033922,0.00008658999,0.00001707259,0.000008652229,0.00003197979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05996704,"threshold_uncertainty_score":0.9904195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04623349752261135,"score_gpt":0.2576949324339109,"score_spread":0.2114614349112995,"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."}}