Screening of Indoor Transformation Products of Organophosphates and Organophosphites with an in Silico Spectral Database
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
High Resolution Image Download MS PowerPoint Slide Numerous transformation products are formed indoors, but they are outside the scope of current chemical databases. In this study, an in silico spectral database was established to screen previously unknown indoor transformation products of organophosphorus compounds (OPCs). An R package was developed that incorporated four indoor reactions to predict the transformation products of 712 seed OPCs. By further predicting MS 2 fragments, an in silico spectral database was established consisting of 3509 OPCs and 28,812 MS 2 fragments. With this database, 40 OPCs were tentatively detected in 23 indoor dust samples. This is the greatest number of OPCs reported to date indoors, among which two novel phosphonates were validated using standards. Twenty-four of the detected OPCs were predicted transformation products in which oxidation from organophosphites plays a major role. To confirm this, the in silico spectral database was expanded to include organophosphites for suspect screening in five types of preproduction plastics. A broad spectrum of 14 organophosphites was detected, with a particularly high abundance in polyvinyl chloride plastics and indoor end-user goods. This demonstrated the significant contribution of organophosphites to indoor organophosphates via oxidation, highlighting the strength of in silico spectral databases for the screening of unknown indoor transformation products.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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