Nontargeted Analysis of Reactive Nitrogenous Compounds in Suwannee River Standard Reference Materials and Authentic River Water Samples
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Concerns over toxic nitrogenous disinfection byproducts (N-DBPs) necessitate identifying their precursors in source water. Natural organic amino compounds are known precursors to N-DBPs. Three Suwannee River (SR) standard reference materials (SRMs), humic acids (HA), fulvic acids (FA), and natural organic matter (NOM), are commonly used to study DBP formation, but the chemical makeup of amino compounds in SRSRMs remains largely unknown. To address this, we combined stable hydrogen/deuterium isotope labeling, HDPairFinder bioinformatics, and nontargeted high-performance liquid chromatography–high-resolution mass spectrometry (HPLC-HRMS) to characterize these compounds in SRSRMs. This method classifies reactive amines, provides accurate masses and MS/MS spectra, and quantifies intensities. We identified 2707 high-quality features with primary and/or secondary amines in SRSRMs and 75% of them having an m / z < 300. Across all three SRSRMs, 327 amino features were detected, while 856, 794, and 200 unique features were found in SRNOM, SRHA, and SRFA, respectively. In North Saskatchewan River (NSR) samples, a total of 6449 amino features were detected, 818 of them matched those in SRSRMs, and 87% of them were different between the two rivers. Using chemical standards, we confirmed 10 compounds and tentatively identified 5 more. This study highlights similarities and differences in reactive N-precursors in SRSRMs and local river water, enhancing the understanding of geo-differences in reactive N-precursors in different source waters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".