Derivation of Occupational Thresholds of Toxicological Concern for Systemically Acting Noncarcinogenic Organic Chemicals
Bibliographic record
Abstract
Many substances in workplace do not have occupational exposure limits. The threshold of toxicological concern (TTC) principle is part of the hierarchy of approaches useful in occupational health risk assessment. The aim of this study was to derive occupational TTCs (OTTCs) reflecting the airborne concentrations below which no significant risk to workers would be anticipated. A reference dataset consisting of the 8-h threshold limit values-Time-Weighted Average for 280 organic substances was compiled. Each substance was classified into low (class I), intermediate (class II), or high (class III) hazard categories as per Cramer rules. For each chemical, n-octanol:water partition coefficient and vapor pressure along with the molecular weight were used to predict the blood:air partition coefficient. The blood:air partition coefficient along with data on water solubility and ventilation rate allowed the prediction of pulmonary retention factor and absorbed dose in workers. For each Cramer class, the distribution of the predicted doses was analyzed to identify the various percentile values corresponding to the OTTC. Accordingly, for Cramer classes I-III, the OTTCs derived in this study correspond to 0.15, 0.0085, and 0.006 mmol/d, respectively, at the 10th percentile level, while these values were 1.5, 0.09 and 0.03 mmol/d at the 25th percentile level. The proposed OTTCs are not meant to replace the traditional occupational exposure limits, but can be used in data-poor situations along with exposure estimates to support screening level risk assessment and prioritization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".