{"id":"W3043350988","doi":"10.1016/j.jglr.2020.06.023","title":"Where you trap matters: Implications for integrated sea lamprey management","year":2020,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; U.S. Fish and Wildlife Service; Great Lakes Fishery Commission","keywords":"Lamprey; STREAMS; Fishery; Environmental science; Abundance (ecology); Trap (plumbing); Petromyzon; Ecology; Biology; Computer science; Environmental engineering","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":[0.006128058,0.0006962804,0.0008419573,0.0005571149,0.001642755,0.004020435,0.002481507,0.004033671,0.009231194],"category_scores_gemma":[0.02040722,0.0003668923,0.0007545383,0.001029539,0.001447206,0.004571971,0.001594193,0.001753911,0.0005667536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005262897,"about_ca_system_score_gemma":0.01299296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1044093,"about_ca_topic_score_gemma":0.1676947,"domain_scores_codex":[0.9963325,0.002383311,0.0001188167,0.0003100402,0.0004039763,0.000451321],"domain_scores_gemma":[0.9922802,0.003603272,0.001178861,0.0001636567,0.001427884,0.001346125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001167789,0.002292238,0.2531643,0.001158276,0.0008578651,0.001495901,0.001897263,0.3297111,0.003776048,0.04307936,0.08488021,0.2765195],"study_design_scores_gemma":[0.0004863844,0.002705138,0.1751868,0.001962421,0.001310228,0.0004183242,0.02987061,0.5781037,0.002314704,0.153837,0.05338469,0.00042007],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4818588,0.006948526,0.03267979,0.4081539,0.001062341,0.0007422697,0.002014305,0.0005403795,0.06599969],"genre_scores_gemma":[0.9701257,0.002416206,0.01258311,0.01019983,0.000154092,0.0002308258,0.0002250268,0.00003711468,0.004028161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1044093,"threshold_uncertainty_score":0.2076031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06598369393812224,"score_gpt":0.337503868838434,"score_spread":0.2715201749003117,"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."}}