{"id":"W2889220879","doi":"10.1139/cjfas-2018-0134","title":"Improving communication: the key to more effective MSE processes","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation; Ocean Foundation; International Seafood Sustainability Foundation; Pew Charitable Trusts","keywords":"Multinational corporation; Tuna; Fisheries management; Key (lock); Process (computing); Business; Computer science; Fishery; Fish <Actinopterygii>; Computer security; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1585677,0.00227531,0.001503618,0.004975957,0.01130695,0.02241327,0.005136559,0.009625681,0.01329124],"category_scores_gemma":[0.2585601,0.001220184,0.001432658,0.003779749,0.01638373,0.03773844,0.02820212,0.01425142,0.004406499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009066138,"about_ca_system_score_gemma":0.02723777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003282707,"about_ca_topic_score_gemma":0.001992162,"domain_scores_codex":[0.7843987,0.1701282,0.008074517,0.006577078,0.02482155,0.005999882],"domain_scores_gemma":[0.6112372,0.2854794,0.02404781,0.02674295,0.037671,0.01482167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001694134,0.0008976131,0.00754279,0.002734073,0.0001818346,0.001211037,0.2494346,0.002969425,0.00653419,0.2077293,0.04535064,0.4752451],"study_design_scores_gemma":[0.0001773885,0.0007845685,0.009518306,0.006232267,0.0001258998,0.0007439702,0.1694483,0.003732758,0.005188363,0.4509739,0.3526775,0.0003967173],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06508677,0.004138542,0.4131967,0.3150986,0.002248763,0.00458418,0.0002589433,0.002280147,0.1931074],"genre_scores_gemma":[0.6070952,0.003852849,0.3422736,0.02153532,0.002056437,0.005708463,0.0003091194,0.0009013016,0.01626775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1585677,"threshold_uncertainty_score":0.8385962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01236885914085614,"score_gpt":0.2156112900714352,"score_spread":0.203242430930579,"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."}}