{"id":"W4398247363","doi":"10.1016/j.jglr.2024.102373","title":"Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal","year":2024,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; Great Lakes Fishery Commission","keywords":"Electrofishing; Grass carp; Fishery; Catch per unit effort; Tributary; Invasive species; Environmental science; Population; Telemetry; Ecology; Abundance (ecology); Biology; Fish <Actinopterygii>; Geography; Computer science; Cartography","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":[0.0005893551,0.000409497,0.0002492734,0.0004582194,0.0002681707,0.0004454525,0.0004443632,0.0001809875,0.0007228233],"category_scores_gemma":[0.001199364,0.0001543972,0.0001719914,0.00021879,0.0002328004,0.0005755114,0.0005544117,0.0002299314,0.0001180952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003901586,"about_ca_system_score_gemma":0.0005309932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007053786,"about_ca_topic_score_gemma":0.02152162,"domain_scores_codex":[0.9997872,0.0000453553,0.00001183518,0.00006183481,0.00005628974,0.00003762009],"domain_scores_gemma":[0.9995539,0.0001119943,0.0001816288,0.00003529807,0.00005122016,0.00006603585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000486787,0.000630863,0.2795227,0.0004594693,0.0001913078,0.0002273367,0.0006935113,0.01579445,0.4644625,0.000441088,0.0004959496,0.2365941],"study_design_scores_gemma":[0.00004293439,0.002528569,0.8901272,0.0000962321,0.0003068582,0.0004487613,0.0008781691,0.06036207,0.04093556,0.0008398673,0.003304271,0.0001294585],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938337,0.0001036728,0.004947008,0.00007145593,0.000004503226,0.00002515217,0.00005314841,0.00006610541,0.0008952908],"genre_scores_gemma":[0.9906584,0.00009809448,0.008533523,0.00004671155,0.000005537469,0.00002899499,0.00007212438,0.000008834786,0.0005477567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007053786,"threshold_uncertainty_score":0.01402545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04828981347483165,"score_gpt":0.3375322066810337,"score_spread":0.289242393206202,"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."}}