{"id":"W2888214789","doi":"10.1016/j.jglr.2018.08.009","title":"Diet and trophic niche space and overlap of Lake Ontario salmonid species using stable isotopes and stomach contents","year":2018,"lang":"en","type":"article","venue":"Journal of Great Lakes Research","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Ministry of Natural Resources and Forestry; University of Windsor","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Johnson and Johnson; Ontario Ministry of Natural Resources and Forestry; New York State Department of Environmental Conservation","keywords":"Trophic level; Biology; Ecology; Alewife; Ecological niche; Salmo; Oncorhynchus; Trout; Forage fish; Foraging; Fishery; Isotope analysis; Brown trout; Predation; Habitat","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001269205,0.0001153611,0.0003638074,0.0001803679,0.0002382077,0.00007497256,0.0001982499,0.00007776749,0.00130632],"category_scores_gemma":[0.0002020589,0.00008837569,0.00004223998,0.0002331444,0.001490796,0.0003397074,0.0003993113,0.0003620897,0.000006780739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001893345,"about_ca_system_score_gemma":0.00004778673,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003086905,"about_ca_topic_score_gemma":0.123875,"domain_scores_codex":[0.998247,0.0002258287,0.0003298197,0.0002134667,0.0006272182,0.0003566779],"domain_scores_gemma":[0.9991527,0.0001982565,0.0001814689,0.0001722412,0.0001419626,0.0001533762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003207049,0.00005175531,0.9665841,0.00001407662,0.00006795053,0.00004743265,0.0009092985,0.00001638758,0.03098678,0.00003256481,0.000563563,0.000405404],"study_design_scores_gemma":[0.000787079,0.001690488,0.9812535,0.00005090742,0.00005196356,0.0001798696,0.0005925587,0.0004848143,0.002856802,0.0003436134,0.01159886,0.0001095644],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960664,0.0002584503,0.0000151635,0.0002441576,0.00004580135,0.00012485,0.000005563485,0.000001723275,0.003237879],"genre_scores_gemma":[0.9934536,0.0003489215,0.0008002347,0.0000219861,0.00006690825,9.773756e-7,3.424385e-7,0.00001006946,0.005296974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1207881,"threshold_uncertainty_score":0.9996066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0617954923597434,"score_gpt":0.3186072957119846,"score_spread":0.2568118033522412,"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."}}