{"id":"W2154370767","doi":"10.1139/cjfas-2013-0056","title":"Sound pressure level weighting of the center of activity method to approximate sequential fish positions from acoustic telemetry","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Texas at Austin; National Oceanic and Atmospheric Administration; New Jersey Department of Transportation","keywords":"Trilateration; Telemetry; Weighting; Hydrophone; Acoustics; Sound pressure; Fish <Actinopterygii>; Mathematics; Statistics; Computer science; Physics; Telecommunications; Biology; Fishery","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":[0.001559915,0.0009444287,0.0004271813,0.001248881,0.0003064884,0.0005390183,0.0008114089,0.0004492604,0.002373963],"category_scores_gemma":[0.006750499,0.0003417598,0.0005972312,0.001290146,0.0003024381,0.0007161177,0.0007130651,0.0007715204,0.0008091138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000415375,"about_ca_system_score_gemma":0.0007906299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003717037,"about_ca_topic_score_gemma":0.006431429,"domain_scores_codex":[0.9990983,0.00020547,0.00007064709,0.000283415,0.0002959287,0.00004620565],"domain_scores_gemma":[0.9978667,0.0007830574,0.0003263103,0.0003186025,0.0006585536,0.00004682261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005451405,0.0002999993,0.02100453,0.0003385972,0.0003767666,0.0001203747,0.0005408135,0.05911031,0.2178526,0.003662669,0.001904854,0.6942434],"study_design_scores_gemma":[0.00006910597,0.0004904473,0.03732759,0.00003831408,0.0001923706,0.0005991505,0.0000958388,0.8449377,0.1058756,0.00277085,0.0074643,0.0001386891],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07519078,0.0001041547,0.9223974,0.00002519646,0.00005812229,0.00009074665,0.0001142863,0.0009921765,0.001027077],"genre_scores_gemma":[0.1933521,0.00009952355,0.8045043,0.00003644347,0.00002680275,0.0002432589,0.0003050221,0.0003325252,0.001100083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003717037,"threshold_uncertainty_score":0.0082497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0253331603739188,"score_gpt":0.2385097707895397,"score_spread":0.2131766104156209,"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."}}