{"id":"W3048312104","doi":"10.1002/fsh.10512","title":"Knowledge co-production: A pathway to effective fisheries management, conservation, and governance","year":2020,"lang":"en","type":"article","venue":"Fisheries","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":172,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Ottawa; The Scarborough Hospital; University of British Columbia; University of Toronto; Fisheries and Oceans Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Genome British Columbia; Genome Canada","keywords":"Business; Corporate governance; Production (economics); Fishery; Fisheries management; Fisheries law; Environmental resource management; Environmental planning; Geography; Environmental science; Biology; Economics; Finance; Fishing","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007380511,0.0001339319,0.0001198663,0.000008119076,0.0001473073,0.00006795551,0.0001217697,0.00001865869,0.00110618],"category_scores_gemma":[0.00006020001,0.0001288515,0.00002237084,0.0002614371,0.0001599068,0.0002887766,0.001254226,0.00005212483,0.0003695354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004660892,"about_ca_system_score_gemma":0.000002624341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001552881,"about_ca_topic_score_gemma":0.000440345,"domain_scores_codex":[0.9991701,0.00002756649,0.0001189404,0.0003789667,0.0001409763,0.0001634047],"domain_scores_gemma":[0.9996845,0.00001792687,0.00003800295,0.0001480894,0.000008340679,0.0001031822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001169277,0.00005240591,0.09397125,0.0001455632,0.00003645399,0.00001648002,0.003436087,0.00001953905,0.0005395541,0.00214984,0.5939027,0.3056132],"study_design_scores_gemma":[0.0001257358,0.000100401,0.2705221,0.00000817607,0.00000724814,0.000001346213,0.0002553393,0.00002106025,0.0005965501,0.0004642112,0.7277678,0.0001300439],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2618382,0.00007126339,0.000888649,0.05681193,0.000376619,0.001359325,0.00002905458,0.000231311,0.6783937],"genre_scores_gemma":[0.9383025,0.0001080513,0.002182666,0.006337882,0.0002030731,0.0003534462,0.00001666499,0.00002921414,0.05246657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6764642,"threshold_uncertainty_score":0.9998069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327578040767676,"score_gpt":0.2054480343945714,"score_spread":0.1921722539868946,"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."}}