{"id":"W4412831424","doi":"10.1021/acs.estlett.5c00669","title":"Uncovering Spatially Resolved 6PPDQ Metabolism in Rainbow Trout Fry with Nano-DESI Mass Spectrometry Imaging","year":2025,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; Vancouver Island University; University of Saskatchewan; University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Canada Foundation for Innovation; Vancouver Island University; University of Victoria","keywords":"Rainbow trout; Mass spectrometry imaging; Mass spectrometry; Rainbow; Nano-; Chemistry; Fish <Actinopterygii>; Environmental chemistry; Fishery; Materials science; Physics; Biology; Chromatography; Optics","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":[0.0001061873,0.0002283738,0.0001236205,0.0002283307,0.0001304625,0.0002152207,0.0001598532,0.0002864357,0.0008151127],"category_scores_gemma":[0.00008320124,0.0001466244,0.0002118324,0.000118053,0.0002388647,0.0002406472,0.0002541772,0.0004340517,0.0002398286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003299189,"about_ca_system_score_gemma":0.0002624815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0032905,"about_ca_topic_score_gemma":0.00473272,"domain_scores_codex":[0.9999478,0.000002024348,0.000001972087,0.00001903348,0.0000184221,0.00001066507],"domain_scores_gemma":[0.999951,0.000005270262,0.00001658587,0.000003490897,0.0000156554,0.000008049259],"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":[0.0000208231,0.000003925296,0.0008792713,0.00001920281,0.000003040584,0.00002638235,0.00001747477,0.00004857564,0.9978223,0.0000580886,0.00004725088,0.001053715],"study_design_scores_gemma":[0.000008990121,0.0001879417,0.04797177,0.00000938284,0.00002401833,0.0002835782,0.0001507435,0.004631169,0.9433738,0.000212797,0.003120538,0.00002529789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731551,0.00112317,0.02044351,0.0002551284,0.00004134327,0.00003879975,0.001329896,0.000257858,0.003355237],"genre_scores_gemma":[0.9553717,0.001655441,0.03328134,0.0003383624,0.00002089797,0.00009329636,0.001168213,0.0000950301,0.007975808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0032905,"threshold_uncertainty_score":0.006542683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003106134296568285,"score_gpt":0.2069418716058077,"score_spread":0.2038357373092394,"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."}}