{"id":"W2808046150","doi":"10.17501/iccc.2017.1201","title":"SHRIMP FARMERS’ COMPETENCE AND TRAINING NEEDS ON CLIMATE CHANGE ADAPTATION: A CASE STUDY FROM SOUTHWEST COASTAL BANGLADESH","year":2018,"lang":"en","type":"article","venue":"International conference on climate change","topic":"Agricultural Innovations and Practices","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre; Department for International Development; Government of the United Kingdom","keywords":"Shrimp; Competence (human resources); Adaptation (eye); Training (meteorology); Climate change; Fishery; Environmental resource management; Geography; Oceanography; Environmental science; Psychology; Meteorology; Geology; Biology; Management; Economics","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":[0.001467997,0.0003494702,0.0003415934,0.0007538135,0.003814411,0.0009173594,0.0004463279,0.0007710478,0.002460234],"category_scores_gemma":[0.002717931,0.0003380183,0.0002224832,0.001162961,0.0009378055,0.0008464181,0.001550065,0.0007715498,0.0002305826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001897155,"about_ca_system_score_gemma":0.001619387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03268667,"about_ca_topic_score_gemma":0.07199197,"domain_scores_codex":[0.9991886,0.0003261411,0.00005656171,0.00006773586,0.0001109566,0.0002499328],"domain_scores_gemma":[0.9981394,0.0007255549,0.0002756458,0.00005641471,0.0002711982,0.0005318102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002311588,0.001037431,0.3864878,0.0002889374,0.000026967,0.03069963,0.5522373,0.0002984417,0.00542717,0.0006557876,0.001054642,0.02155475],"study_design_scores_gemma":[0.00001866281,0.0005600837,0.1525362,0.0001069255,0.00001941526,0.003116111,0.8397637,0.0002900351,0.0004781136,0.0001489366,0.002932259,0.00002961638],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993026,0.00002007652,0.00003161819,0.0001345621,8.694924e-7,0.00001618779,0.00001271818,4.467993e-7,0.0004809525],"genre_scores_gemma":[0.9991252,0.0001699875,0.0001274809,0.00006245225,0.000001664342,0.00002605603,0.00001726639,7.45837e-7,0.0004691568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03268667,"threshold_uncertainty_score":0.06499285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3318528867600725,"score_gpt":0.3433245187668195,"score_spread":0.01147163200674706,"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."}}