Comparative toxicity of conventional and unconventional oils during rainbow trout (Oncorhynchus mykiss) embryonic development: From molecular to health consequences
Bibliographic record
Abstract
Canadian freshwater ecosystems are vulnerable to oil spills from pipelines, which contain mostly diluted bitumen. This study aimed to compare the toxicity of a dilbit and a conventional oil on developing rainbow trout. A total of five exposure scenarios were performed, from 10 to 43 days, using water-accommodated fraction (WAF) with an initial loading of 1:9 oil to water ratio (w/v) in a range of dilutions from 0.32 to 32% WAF, respectively, with TPAH and VOC concentrations from 2.41 to 17.5 μg/L and 7.94-660.99 μg/L, and with or without a recovery period. Following the five exposures, several endpoints were examined, including survivorship, morphometrics, gene expression, and enzymatic activity. Significant mortality rates were measured for the highest WAF concentration of the dilbit in all five exposures (60-100% mortality at 32% WAF). In comparison, the highest WAF concentration of the conventional oil induced significant mortality in three out of the five exposure (from 35 to 100% mortality at 32% WAF). Hatching delays were noted in embryos exposed to both oils. Developmental delays were observed in dilbit-exposed embryos and are suspected to be an indicator of reduced survivorship after hatching. The induced expression of cyp1a remained a reliable biomarker of exposure and of fish malformations, though it did not always predict mortality. Using CYP1A activity in combination with cyp1a may bring more insights in studies of oil risk assessment. This study demonstrates that dilbits are more toxic to early life stages compared to conventional oils and highlights the need to consider the most sensitive stage of development when performing risk assessment studies on oils.
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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".