{"id":"W4296808103","doi":"10.1002/mrc.5315","title":"A holistic NMR framework to understand environmental impact: Examining the impacts of superparamagnetic iron oxide nanoparticles (SPIONs) in <scp><i>Daphnia magna</i></scp> via imaging, spectroscopy, and metabolomics","year":2022,"lang":"en","type":"article","venue":"Magnetic Resonance in Chemistry","topic":"Electrochemical Analysis and Applications","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Krembil Foundation; Canada Foundation for Innovation; Centre National de la Recherche Scientifique; Government of Ontario","keywords":"Daphnia magna; Metabolomics; Chemistry; Nuclear magnetic resonance spectroscopy; Metabolome; Environmental chemistry; Daphnia; Relaxometry; Toxicity; Nanotechnology; Magnetic resonance imaging; Ecology; Organic chemistry; Chromatography; Biology","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":[0.0003023201,0.0005349983,0.0004455459,0.0006738549,0.0003566734,0.0008375907,0.0005070927,0.0006529258,0.0009449341],"category_scores_gemma":[0.0002566705,0.0001713706,0.0004071749,0.0003913137,0.0008589657,0.001162398,0.0009294669,0.0006676491,0.0002091192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008861958,"about_ca_system_score_gemma":0.0007217114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003224264,"about_ca_topic_score_gemma":0.005402161,"domain_scores_codex":[0.9999037,0.00001939224,0.000004491512,0.00002935511,0.00002276831,0.00002023434],"domain_scores_gemma":[0.9999263,0.00001863644,0.00001415173,0.000007243834,0.00002335985,0.00001026321],"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":[0.0001169111,0.0001141232,0.006959412,0.001277775,0.0001511466,0.0007348494,0.0006400766,0.04537775,0.8476756,0.03702999,0.001701719,0.05822059],"study_design_scores_gemma":[0.00003916306,0.001618296,0.07350947,0.0007865761,0.0008914793,0.001746875,0.005221607,0.2745405,0.3662262,0.1559398,0.1190983,0.0003818393],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4588889,0.01783645,0.4636914,0.005307088,0.0002759299,0.0002635212,0.003354269,0.001005085,0.04937741],"genre_scores_gemma":[0.8616655,0.01218646,0.1200835,0.001145211,0.00006243154,0.0002716393,0.0007651793,0.00007589528,0.003744071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003224264,"threshold_uncertainty_score":0.006429851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00873779186701283,"score_gpt":0.2400332466478666,"score_spread":0.2312954547808538,"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."}}