{"id":"W4237899084","doi":"10.7287/peerj.preprints.2291v1","title":"Enhancing fisheries education in Canada: The need for interdisciplinarity, collaboration, and inclusivity","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Conservation, Ecology, Wildlife Education","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Université Laval; University of British Columbia; University of Guelph; St. Mary's University; University of New Brunswick; Saint Mary's University; McGill University","funders":"","keywords":"Fisheries management; Fisheries law; Government (linguistics); Fishery; Fisheries Research; Fisheries science; Political science; Fish <Actinopterygii>; Fishing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01063585,0.0002602891,0.0004528603,0.002591801,0.01182546,0.009345633,0.001595099,0.001748889,0.003522217],"category_scores_gemma":[0.02613216,0.0002626086,0.0003960306,0.004719355,0.005248741,0.004218275,0.006377576,0.002748162,0.0002099215],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07798643,"about_ca_system_score_gemma":0.4479854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.968386,"about_ca_topic_score_gemma":0.9886137,"domain_scores_codex":[0.9913855,0.002316803,0.0004027328,0.0005177733,0.002980796,0.002396412],"domain_scores_gemma":[0.9649187,0.008586323,0.002200558,0.0006256322,0.01083821,0.01283045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001044747,0.0002482751,0.07844897,0.008354099,0.0001319145,0.001642934,0.07301517,0.001261257,0.002243464,0.06890266,0.06775541,0.6978914],"study_design_scores_gemma":[0.00006481566,0.0002003124,0.2539184,0.01285237,0.0001711479,0.0005271318,0.1308882,0.0009610817,0.001208241,0.02564959,0.573348,0.0002106604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2507041,0.06222254,0.006843946,0.5480173,0.001203063,0.0004353072,0.0006892426,0.0002002836,0.1296843],"genre_scores_gemma":[0.9205442,0.02868922,0.01452434,0.02764774,0.0001164045,0.0001480514,0.0002419884,0.00004002612,0.008047929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9220136,"threshold_uncertainty_score":0.5658338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009159629238152634,"score_gpt":0.2629337801366624,"score_spread":0.2537741508985097,"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."}}