Effects-driven chemical fractionation of heavy fuel oil to isolate compounds toxic to trout embryos
Bibliographic record
Abstract
Heavy fuel oil (HFO) spills account for approximately 60% of ship-source oil spills and are up to 50 times more toxic than medium and light crude oils. Heavy fuel oils contain elevated concentrations of polycyclic aromatic hydrocarbons (PAHs) and alkyl-PAHs, known to be toxic to fish; however, little direct characterization of HFO toxicity has been reported. An effects-driven chemical fractionation was conducted on HFO 7102 to separate compounds with similar chemical and physical properties, including toxicity, to isolate the groups of compounds most toxic to trout embryos. After each separation, toxicity tests directed the next phase of fractionation, and gas chromatography-mass spectrometry analysis correlated composition with toxicity, with a focus on PAHs. Low-temperature vacuum distillation permitted the separation of HFO into 3 fractions based on boiling point ranges. The most toxic of these fractions underwent wax precipitation to remove long-chain n-alkanes. The remaining PAH-rich extract was further separated using open column chromatography, which provided distinct fractions that were grouped according to increasing aromatic ring count. The most toxic of these fractions was richest in PAHs and alkyl-PAHs. The results of the present study were consistent with previous crude oil studies that identified PAH-rich fractions as the most toxic.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".