Chronic toxicity of heavy fuel oils to fish embryos using multiple exposure scenarios
Bibliographic record
Abstract
The chronic toxicity to rainbow trout (Oncorhynchus mykiss) embryos of heavy fuel oil (HFO) 6303, weathered HFO 6303, HFO 7102, and medium South American (MESA) crude oil was assessed by different exposure regimes. These included water accommodated fractions (WAF; water in contact with floating oil), chemically enhanced WAF (CEWAF; oil dispersed with Corexit 9500), and effluent from columns of gravel coated with stranded oil. Heavy fuel oil WAF was nontoxic and did not contain detectable concentrations of hydrocarbons, likely because the high density and viscosity of HFO prevented droplet formation. In contrast, chemically dispersed HFO and effluent from columns of stranded HFO contained measurable concentrations of alkyl polycyclic aromatic hydrocarbons (PAH), coincident with embryo toxicity. These exposure regimes enhanced the surface area of oil in contact with water, facilitating oil-water partitioning of hydrocarbons. Heavy fuel oil was consistently more toxic to fish than crude oil and the rank order of alkyl PAH concentrations in whole oil were sufficient to explain the rank order of toxicity, regardless of exposure method. Thus, the propensity of HFO to sink and strand in spawning shoals creates a long-term risk to developing fish because of the sustained release of PAHs from HFO to interstitial waters. Further, PAH monitoring is key to accurate risk assessment.
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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 | 0.000 | 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) | 0.001 | 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".