{"id":"W2970463680","doi":"10.1016/j.watres.2019.115033","title":"Daphnia magna metabolic profiling as a promising water quality parameter for the biological early warning system","year":2019,"lang":"en","type":"article","venue":"Water Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Ministry of the Environment, Government of Japan","keywords":"Metabolomics; Metabolome; Daphnia magna; Water quality; Biomonitoring; Biology; Daphnia; Pollutant; Organism; Metabolite profiling; Omics; Environmental chemistry; Ecology; Bioinformatics; Chemistry; Toxicity","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":[0.004063315,0.0002012313,0.0003344748,0.00009678046,0.0004249221,0.0001606458,0.0004454535,0.0001691755,0.00004542292],"category_scores_gemma":[0.0002546246,0.00008876708,0.0001831335,0.00009650752,0.0001542425,0.000007626832,0.0006078504,0.0003099277,0.0001936763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002217938,"about_ca_system_score_gemma":0.00003046503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001214206,"about_ca_topic_score_gemma":0.000004152558,"domain_scores_codex":[0.9972489,0.0004730527,0.0003358769,0.0006370063,0.0003459591,0.0009592439],"domain_scores_gemma":[0.9989266,0.00007379962,0.0000388789,0.0006006938,0.0002780676,0.00008192716],"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":[0.0002391509,0.00002820454,0.004927753,0.00008869268,0.0001739654,0.00000148598,0.0002742176,0.000005838942,0.99207,0.00160162,0.00005719379,0.0005319458],"study_design_scores_gemma":[0.00058784,0.0004309835,0.002185643,0.00001302074,0.00001877198,0.000008281658,0.0005383387,0.00009262159,0.9561641,0.0003484353,0.03941287,0.0001991078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954255,0.001173878,0.000428366,0.0004793089,0.0002132177,0.001491504,0.00001398325,0.00002309122,0.0007510987],"genre_scores_gemma":[0.9954336,0.0001225246,0.000897963,0.00005819209,0.0003124672,0.0003979168,0.00007367556,0.00003237133,0.002671323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03935567,"threshold_uncertainty_score":0.3619816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09551008934114316,"score_gpt":0.3796287898624628,"score_spread":0.2841187005213196,"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."}}