{"id":"W2993742115","doi":"10.3996/022019-jfwm-013","title":"Environmental and biological factors influence migratory Sea Lamprey catchability: implications for tracking abundance in the Laurentian Great Lakes","year":2019,"lang":"en","type":"article","venue":"Journal of Fish and Wildlife Management","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Great Lakes Fishery Commission","keywords":"Lamprey; Petromyzon; Tributary; Abundance (ecology); Mark and recapture; Fishery; Environmental science; Population; Ecology; Biology; Geography; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0004282342,0.0001135667,0.0001635218,0.00004405718,0.0001516616,0.00003516356,0.0001953854,0.00004005255,0.00005926673],"category_scores_gemma":[0.00001742485,0.00007516014,0.00004591278,0.00006118367,0.0001984414,0.0002264104,0.0001466609,0.0001056963,0.000003631308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004635369,"about_ca_system_score_gemma":0.00000170161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001392307,"about_ca_topic_score_gemma":0.0007250713,"domain_scores_codex":[0.9992183,0.00005125357,0.0002424015,0.0001965556,0.0001085031,0.0001829766],"domain_scores_gemma":[0.9995667,0.0001240146,0.0001378921,0.0001257418,0.000003641191,0.0000420404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001907429,0.00007232111,0.9690295,0.00002390475,0.00003446167,0.000002297971,0.0005363497,0.0000515391,0.00003300906,0.00009341342,0.02861929,0.001484814],"study_design_scores_gemma":[0.0003951762,0.0001429561,0.9197295,0.00001204821,0.00002913247,0.000004537787,0.001184596,0.000008270651,0.000003052207,0.0006238425,0.07778697,0.00007988574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872313,0.00004356285,0.00003698425,0.01168865,0.00005221082,0.0004614292,0.00001305462,0.000004422674,0.0004684171],"genre_scores_gemma":[0.9896403,0.0006215386,0.0001237516,0.009486308,0.00001474334,0.0000235676,0.000003708355,0.000003960636,0.00008211466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04929999,"threshold_uncertainty_score":0.3064942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273739682943712,"score_gpt":0.2233569592135289,"score_spread":0.2106195623840917,"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."}}