Evaluating toxicity of heavy fuel oil fractions using complementary modeling and biomimetic extraction methods
Bibliographic record
Abstract
The toxicity of chemically dispersed heavy fuel oil (HFO) and 3 distillate fractions to rainbow trout (Oncorhynchus mykiss) embryos was evaluated using the PETROTOX model and a biomimetic extraction technique that involved passive sampling of oil-contaminated test media with solid-phase microextraction (SPME) fibers. Test solutions for toxicity testing were generated using a combination of dispersant and high-energy mixing. The resulting water accommodated fractions (WAF) provided complex exposure regimens that included both dissolved hydrocarbons and oil droplets. The toxicity of the various fractions differed by approximately 3 orders of magnitude when expressed on the basis of WAF dilution. Using detailed compositional data, the PETROTOX model predicted the speciation of hydrocarbons between dissolved and oil droplet phases and explained observed toxicity based on computed dissolved phase toxic units (TUs). A key finding from model calculations was that dissolved hydrocarbon exposures and associated TUs were a nonlinear function of WAF dilution, because dissolved hydrocarbons were largely controlled by the dissolution of oil droplets that were transferred in WAF dilutions. Hence, oil droplets served to "buffer" dissolved concentrations in WAF dilutions at loadings greater than 1 mg/L, resulting in higher dissolved concentrations and TUs than expected based on dilution. The TUs computed at each WAF dilution explained the observed toxicity among the HFO and fractions to within a factor of 3. Dissolved material measured by SPME showed a consistent relationship with model-predicted TUs, confirming the utility of this approach for providing an integrated measure of exposure to bioavailable hydrocarbons. These 2 approaches provide complementary tools for better defining bioavailability of complex petroleum substance.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".