{"id":"W3022341686","doi":"10.1007/s13280-020-01327-7","title":"Governing offshore fish aggregating devices in the Eastern Caribbean: Exploring trade-offs using a qualitative network model","year":2020,"lang":"en","type":"article","venue":"AMBIO","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Corporate governance; Incentive; Food security; Business; Fishery; Overfishing; Fisheries management; Environmental resource management; Overexploitation; Provisioning; Livelihood; Economics; Fishing; Geography; Agriculture; Engineering; Finance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002935841,0.0001461359,0.0001971643,0.000007145171,0.0002147925,0.00004667157,0.0002309665,0.00002545886,0.00001916757],"category_scores_gemma":[0.00005115713,0.0001100175,0.00005457019,0.0002840855,0.00005123292,0.0003001562,0.0003421932,0.0001720403,0.00002835837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008296438,"about_ca_system_score_gemma":0.000006531294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919343,"about_ca_topic_score_gemma":0.005895249,"domain_scores_codex":[0.9987797,0.0001355887,0.0002430816,0.0002794709,0.0002566803,0.0003055387],"domain_scores_gemma":[0.9996045,0.0001178768,0.00011598,0.0001085862,0.000002990285,0.00005008974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004075045,0.00004543926,0.6170825,0.0000990455,0.00004724432,0.00004241595,0.2871617,0.07905522,0.0005101573,0.0001458943,0.003262418,0.01250716],"study_design_scores_gemma":[0.0004072266,0.0001006033,0.06349316,0.0002152545,0.00003937867,0.000006731338,0.07793913,0.8521719,0.00005601239,0.0003078781,0.004801862,0.0004608785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942257,0.0000951946,0.0004510397,0.002551028,0.00006844257,0.0001855237,0.000007253673,0.00003016266,0.002385634],"genre_scores_gemma":[0.9978126,0.00001022912,0.0006941402,0.001252948,0.0001519437,0.00003002657,0.00000286948,0.00001178958,0.00003341955],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7731166,"threshold_uncertainty_score":0.4486385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1366415168104951,"score_gpt":0.2894099389853035,"score_spread":0.1527684221748084,"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."}}