{"id":"W4409417277","doi":"10.1139/cjfas-2024-0335","title":"Integrating acoustic telemetry research into management: successes and challenges in the Laurentian Great Lakes","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Fisheries and Oceans Canada; Queen's University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telemetry; Fishery; Geography; Ecology; Oceanography; Environmental resource management; Environmental science; Biology; Telecommunications; Engineering; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003251069,0.00008682108,0.0001452759,0.0007786999,0.0007271817,0.0007644786,0.0005525415,0.0000356962,0.0001665557],"category_scores_gemma":[0.0004020672,0.00005169163,0.00001683144,0.0008963516,0.001240973,0.0002720192,0.00001821783,0.0002737172,0.000001374851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001189275,"about_ca_system_score_gemma":0.000413328,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02605818,"about_ca_topic_score_gemma":0.7590531,"domain_scores_codex":[0.9985384,0.0002393563,0.0002386838,0.0001738043,0.00041952,0.0003902104],"domain_scores_gemma":[0.9988325,0.0008034217,0.00004800859,0.0000886436,0.00005513259,0.000172257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001706662,0.000007878904,0.6294703,0.0003236793,0.00003339232,0.0002469196,0.008113134,0.0002664918,0.000007156749,0.0009223165,0.001316715,0.359275],"study_design_scores_gemma":[0.0007698475,0.001291357,0.5487394,0.001350127,0.00006932832,0.0002520864,0.2683482,0.05417644,0.00003185712,0.08003182,0.04451967,0.0004198386],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9112266,0.02817369,0.0004372802,0.03338879,0.0002985755,0.0002621168,0.000006027692,0.000003915159,0.02620306],"genre_scores_gemma":[0.9957809,0.002840637,0.001113588,0.0001233009,0.00003467096,8.481849e-7,8.769844e-7,0.00000128645,0.0001038223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7329949,"threshold_uncertainty_score":0.9804274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06880945622540476,"score_gpt":0.2946567397953935,"score_spread":0.2258472835699888,"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."}}