Integrating Mass Spectrometry and Computational Chemistry for the Identification of Persistent and Bioaccumulative Organic Compounds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it