{"id":"W2765287779","doi":"10.1139/cjfas-2017-0303","title":"Applying a knowledge–action framework for navigating barriers to incorporating telemetry science into fisheries management and conservation: a qualitative study","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telemetry; Action (physics); Relevance (law); Corporate governance; Resource (disambiguation); Environmental resource management; Human Dimension; Dimension (graph theory); Fisheries management; Fishery; Knowledge management; Ecology; Business; Computer science; Biology; Political science; Fishing; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.07899899,0.0006325838,0.0007791812,0.00347256,0.0139417,0.007558057,0.002906526,0.002755765,0.003011098],"category_scores_gemma":[0.05289275,0.0008228763,0.0004332309,0.00214053,0.02635266,0.011386,0.008853261,0.003991039,0.0002662211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01382611,"about_ca_system_score_gemma":0.01644301,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01770704,"about_ca_topic_score_gemma":0.02147227,"domain_scores_codex":[0.9463139,0.04624315,0.001176917,0.001507784,0.002205597,0.002552595],"domain_scores_gemma":[0.9114186,0.07370993,0.004124926,0.002081886,0.005679747,0.002984945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001497785,0.00004827658,0.00192679,0.0001471724,0.000003831685,0.0003212004,0.9822834,0.00009124767,0.0004113168,0.01105308,0.0003110216,0.003387633],"study_design_scores_gemma":[0.000008193107,0.0000237844,0.000509462,0.0001801877,0.00000357931,0.00008200201,0.989823,0.0003302882,0.0002056815,0.003649132,0.005172611,0.00001193685],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9068626,0.0006104663,0.04761027,0.02054386,0.0001318178,0.001804822,0.0002552398,0.00004685178,0.02213401],"genre_scores_gemma":[0.9841069,0.0002854031,0.01087026,0.001718449,0.00001093471,0.001150132,0.00004262745,0.00002286113,0.001792405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9860583,"threshold_uncertainty_score":0.4177916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0603327346613181,"score_gpt":0.3637753695440903,"score_spread":0.3034426348827722,"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."}}