1. Toxicity of Fresh and Weathered Sunken Heavy Fuel Oil to the Early Life Stages of Rainbow Trout (Oncorhynchus mykiss) in Simulated Spawning Shoal Environments
Bibliographic record
Abstract
Because the density of heavy fuel oil (HFO) is equal to or greater than that of freshwater, it behaves differently than lighter oils that float. Heavy fuel oil can sink to the bottom or be suspended in the water column and affect aquatic organisms that are not typically exposed to floating oils. Most research on oil spill technologies thus far examines the direct exposure of rainbow trout to floating or submerged oil droplets; there is little knowledge of the impacts of non‐floating heavy fuel oil on the water column and benthic organisms exposed to oil that accumulates in sediments. The toxicity of sunken HFO 6303 and Medium South American (MESA; reference) crude oil, as well as the effects of weathering on toxicity to embryos of rainbow trout were assessed using increasing concentrations of oil on gravel substrate in continuous‐flow desorption columns. Toxicity was assessed by measurement of the rates of mortality and growth, and the prevalence of blue sac disease, a hallmark sign of oil toxicity. The lower median lethal concentration for HFO compared to MESA indicated that HFO is more toxic. Interestingly, the LC50 values for fresh and weathered for both oils were similar, indicating little change in toxicity when the oil weathers naturally. Repetition of this experiment and analysis of PAH content in each treatment will provide more insight into the environmental and health risks associated with sunken heavy fuel oil.
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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".