{"id":"W2785341407","doi":"10.1002/9780470034590.emrstm1534","title":"NMR-Based Metabolomics of <i>Daphnia Magna</i>: Insights into Aquatic Ecosystem Health","year":2017,"lang":"en","type":"reference-entry","venue":"eMagRes","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Krembil Foundation","keywords":"Daphnia magna; Metabolomics; Aquatic ecosystem; Biology; Context (archaeology); Ecotoxicology; Metabolome; Daphnia; Ecology; Pollutant; Ecosystem health; Ecosystem; Environmental chemistry; Bioinformatics; Chemistry; Crustacean; Toxicity","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":[0.0001631128,0.0003373961,0.0002013777,0.0005229855,0.0002189198,0.000476193,0.0001642592,0.0004408848,0.00152706],"category_scores_gemma":[0.0001200978,0.0001296015,0.0001812744,0.000424081,0.000205604,0.0004396872,0.0003280838,0.0003900362,0.0006173934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004742355,"about_ca_system_score_gemma":0.0003031378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002483505,"about_ca_topic_score_gemma":0.005114879,"domain_scores_codex":[0.9999363,0.000006861062,0.000003171825,0.00001792035,0.0000225988,0.00001302291],"domain_scores_gemma":[0.9999299,0.000009457392,0.00001765839,0.000004821729,0.00002661348,0.0000116333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009201083,0.00001182751,0.001849562,0.0002653466,0.00001758739,0.00008891398,0.00003714448,0.0001285131,0.9823672,0.0002857405,0.001287216,0.01356891],"study_design_scores_gemma":[0.00001900913,0.0003038996,0.1044093,0.0001323897,0.0001433135,0.0009540877,0.0003852212,0.002067842,0.8193574,0.001502731,0.07064287,0.00008196213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8633139,0.03252166,0.03132745,0.004538846,0.0003896953,0.000118817,0.02677286,0.0008254895,0.04019128],"genre_scores_gemma":[0.9221216,0.02396447,0.02467916,0.001728493,0.0001291321,0.00005587746,0.01086607,0.0001106954,0.01634433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002483505,"threshold_uncertainty_score":0.005108535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809027124715964,"score_gpt":0.2785497414614206,"score_spread":0.260459470214261,"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."}}