{"id":"W2941426785","doi":"10.1139/cjfas-2018-0450","title":"Collaborative fisheries research: the Canadian Fisheries Research Network experience","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Fisheries and Oceans Canada; Canadian Respiratory Research Network; Government of New Brunswick; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries Research and Development Corporation","keywords":"Fisheries management; Scope (computer science); Fisheries Research; Sustainability; Fisheries law; Fishery; Relevance (law); Business; Government (linguistics); Fisheries science; Fish <Actinopterygii>; Environmental resource management; Collaborative network; Knowledge management; Fishing; Political science; Ecology; Computer science; Economics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.05844424,0.0004649112,0.0006330599,0.003349785,0.02404966,0.01063732,0.004423256,0.003556069,0.006862897],"category_scores_gemma":[0.04875422,0.0006463502,0.0005455217,0.01314722,0.01011409,0.006821448,0.01398816,0.00344253,0.0006679412],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0940116,"about_ca_system_score_gemma":0.3085378,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9298708,"about_ca_topic_score_gemma":0.9708157,"domain_scores_codex":[0.9603807,0.01312734,0.001635364,0.002909905,0.01630873,0.005637993],"domain_scores_gemma":[0.9200137,0.01809155,0.002624072,0.00434386,0.02426737,0.03065941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001769138,0.0002609873,0.02391928,0.001499711,0.0001261062,0.002126536,0.1461095,0.001120279,0.001008076,0.1271893,0.3708965,0.3255669],"study_design_scores_gemma":[0.00002692519,0.00005254205,0.01649636,0.0008221445,0.00002800044,0.0003481079,0.06189391,0.0003155861,0.0002162536,0.006177005,0.9135402,0.00008297895],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1456853,0.07594623,0.01001478,0.3896141,0.004882988,0.0007186552,0.002580767,0.0003993355,0.3701579],"genre_scores_gemma":[0.7852044,0.08388734,0.02418747,0.02451573,0.0007317173,0.0006150535,0.002505357,0.0003869926,0.07796599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9759504,"threshold_uncertainty_score":0.6821051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07701369127868692,"score_gpt":0.3160162144622622,"score_spread":0.2390025231835753,"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."}}