Screening of Indoor Transformation Products of Organophosphates and Organophosphites with an in Silico Spectral Database
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".