Increased Temperature and Turbulence Alter the Effects of Leachates from Tire Particles on Fathead Minnow (<i>Pimephales promelas</i>)
Bibliographic record
Abstract
Tire particles are of concern as a stressor due to the combination of their chemical constituents, high emission rates, and global distribution. Once in the environment, they will interact with physical parameters (e.g., UV, temperature). The interaction of chemical pollution with changing physical environmental parameters is often underestimated in ecotoxicology. Here, we investigate the role of temperature, mechanical stress (i.e., turbulence), UV, and CO 2 on the effects of tire leachates on fish. Two samples of tire particles were exposed to four different levels of each physical stressor. A toxicological assessment was performed with fathead minnow embryos assessing five end points (hatching success, time to hatch, length, deformities, and heart rate). Results showed that variations of temperature and mechanical stress affect the toxicological impact of tire leachates. Zn and/or polycyclic aromatic hydrocarbons (pyrene, phenanthrene, chrysene, benzo[ a ]pyrene, anthracene, naphthalene, fluoranthene, and benzo[ghi]perylene) were identified in the leachate and tire samples by Raman/surface-enhanced Raman spectroscopy and gas chromatography with mass spectroscopy, respectively.
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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".