{"id":"W4405554517","doi":"10.1016/j.scitotenv.2024.178069","title":"Trophic magnification rates of eighteen trace elements in freshwater food webs","year":2024,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Global Water Futures; Natural Sciences and Engineering Research Council of Canada; Government of Saskatchewan; Canada First Research Excellence Fund; National Geographic Society; Instituto Nacional de Pesquisas da Amazônia; University of Saskatchewan","keywords":"Trophic level; TRACE (psycholinguistics); Environmental science; Magnification; Ecology; Geography; Biology; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004181801,0.0002147614,0.0002058441,0.00125709,0.0004753146,0.0004257206,0.0002460235,0.000319889,0.001183052],"category_scores_gemma":[0.001986015,0.0002302699,0.0003071027,0.0005816619,0.0003613993,0.0003868194,0.0006238709,0.0001800361,0.000154972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000699057,"about_ca_system_score_gemma":0.000239447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006884648,"about_ca_topic_score_gemma":0.01065732,"domain_scores_codex":[0.9998848,0.00001418087,0.00001170948,0.00004054407,0.00002690511,0.00002181456],"domain_scores_gemma":[0.9991475,0.0002532779,0.0003383965,0.00007734408,0.00009745901,0.00008605618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001530045,0.0000163185,0.9748,0.00003390813,0.0000883654,0.0001386504,0.0004461165,0.001114083,0.0151387,0.0001490904,0.0000680625,0.007853673],"study_design_scores_gemma":[0.000001693063,0.00003755271,0.9975968,0.000003030143,0.00001110715,0.0000831493,0.00009507275,0.001528325,0.0004580292,0.00009619611,0.00008281771,0.000006375828],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996848,0.00002366912,0.00007003573,0.000004245065,1.92998e-7,0.000001237045,0.0000538308,0.000004786265,0.0001572312],"genre_scores_gemma":[0.9995203,0.00002253703,0.0002177505,0.000002538534,4.011036e-7,0.000002803365,0.0001131978,0.000001627484,0.0001188314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006884648,"threshold_uncertainty_score":0.0136891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101421515096456,"score_gpt":0.2240737428086874,"score_spread":0.2139315912990418,"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."}}