{"id":"W3184959754","doi":"10.1111/faf.12595","title":"Climate change adaptation in fisheries","year":2021,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Science Research Council","keywords":"Climate change; Fisheries management; Livelihood; Fishing; Fisheries science; Diversification (marketing strategy); Environmental resource management; Adaptation (eye); Adaptive management; Fisheries law; Global warming; Fishery; Geography; Adaptive capacity; Ecological forecasting; Ecology; Business; Environmental science; Biology; Agriculture","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":[0.00005663664,0.00008438372,0.0001224269,0.0000103238,0.00009824372,0.0000447047,0.00003653879,0.00003177628,0.0007809583],"category_scores_gemma":[0.00003149124,0.00007860016,0.00001833713,0.0001309366,0.00006979681,0.0003442403,0.0002445724,0.00005028243,0.00002808442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001977435,"about_ca_system_score_gemma":0.000002409666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00058453,"about_ca_topic_score_gemma":0.02564246,"domain_scores_codex":[0.9994106,0.00002150235,0.0001176411,0.0001873013,0.00008052815,0.0001824293],"domain_scores_gemma":[0.9998342,0.00001923467,0.00002597993,0.00008003695,0.00000628412,0.00003424334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001303229,0.00002127812,0.9297441,0.00002885008,0.000002545897,0.00003014196,0.002178871,4.116401e-7,0.00003673883,0.00007333123,0.008335992,0.05953475],"study_design_scores_gemma":[0.0001428444,0.00002910638,0.8681508,0.00001697673,0.000003181446,0.000008837845,0.001249888,0.000108324,0.00005703247,0.0003742885,0.1297521,0.0001065753],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9495876,0.0001295722,0.000005851999,0.005678526,0.000197321,0.0001053099,0.00001893024,0.00003667513,0.04424025],"genre_scores_gemma":[0.9963217,0.0009058948,0.000227737,0.001003859,0.00006496418,0.00007336828,0.00001913383,0.000007351847,0.001375962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1214161,"threshold_uncertainty_score":0.992137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452358704151469,"score_gpt":0.1987407998775721,"score_spread":0.1642172128360574,"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."}}