Salmon Olfaction is Impaired by an Environmentally Realistic Pesticide Mixture
Bibliographic record
Abstract
Many of the salmon-producing waterways of the world contain pesticides known to harm olfactory sensory neurons (OSNs) that are critically important throughout the salmon lifecycle. The ability of OSNs to retain functionality after exposure to complex pesticide mixtures remains unknown. Here we show that a 96-h exposure to an environmentally realistic concentration of a mixture made from the ten most frequently occurring pesticides in British Columbia's Nicomekl River reduced the OSN responses of rainbow troutto a behaviorally relevant odorant. Odor-evoked responses were not altered by exposure to one-fifth of the realistic concentration, and this may have been due an upregulation in detoxification enzymes, since glutathione-S-transferase activity reached a maximum (> 32% above control) at this concentration. Mixture exposure did not help to prevent OSN impairment from a second, brief (5 min) exposure to a higher (20 x) concentration of the mixture, suggesting longer-term, low-concentration exposures may not prevent damage from brief, high-concentration pulse exposures. This study demonstrates that environmentally observed pesticide mixtures can injure salmon olfactory tissue, and by extension, contribute to the threatened and endangered status of many salmon stocks.
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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 | 0.001 |
| 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 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".