Integrating Mass Spectrometry and Computational Chemistry for the Identification of Persistent and Bioaccumulative Organic Compounds
Bibliographic record
Abstract
The environmental fate and behavior of many persistent, bioaccumulative, and toxic (PB) compounds are unknown, requiring better analytical tools for detection in the environment. Nontargeted screening (NTS) enables measurement of numerous compounds while mass spectral prediction may be useful when reference standards and spectra are unavailable. A combined suspect screening and NTS method using high-resolution mass spectrometry was developed and used to screen electronics waste dust for suspected PB compounds. Two different computational mass spectral prediction methods were tested with 35 PB compounds. The screening identified 67 compounds or formulae, suggesting utility as an exploratory tool for identifying unknown PB compounds. One computational method produced good matches to two recently identified compounds, suggesting benefit for identifying unknown compounds. The results of this thesis suggest that suspect screening, NTS, and mass spectral prediction may be effective tools for detection and identification of PB compounds in the environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".