Development of a chronic noncancer oral reference dose and drinking water screening level for sulfolane using benchmark dose modeling
Bibliographic record
Abstract
Sulfolane is a widely used industrial solvent that is often used for gas treatment (sour gas sweetening; hydrogen sulfide removal from shale and coal processes, etc.), and in the manufacture of polymers and electronics, and may be found in pharmaceuticals as a residual solvent used in the manufacturing processes. Sulfolane is considered a high production volume chemical with worldwide production around 18 000-36 000 tons per year. Given that sulfolane has been detected as a contaminant in groundwater, an important potential route of exposure is tap water ingestion. Because there are currently no federal drinking water standards for sulfolane in the USA, we developed a noncancer oral reference dose (RfD) based on benchmark dose modeling, as well as a tap water screening value that is protective of ingestion. Review of the available literature suggests that sulfolane is not likely to be mutagenic, clastogenic or carcinogenic, or pose reproductive or developmental health risks except perhaps at very high exposure concentrations. RfD values derived using benchmark dose modeling were 0.01-0.04 mg kg(-1) per day, although modeling of developmental endpoints resulted in higher values, approximately 0.4 mg kg(-1) per day. The lowest, most conservative, RfD of 0.01 mg kg(-1) per day was based on reduced white blood cell counts in female rats. This RfD was used to develop a tap water screening level that is protective of ingestion, viz. 365 µg l(-1). It is anticipated that these values, along with the hazard identification and dose-response modeling described herein, should be informative for risk assessors and regulators interested in setting health-protective drinking water guideline values for sulfolane.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".