Effects of agricultural pesticides on the immune system of <i>Rana pipiens</i> and on its resistance to parasitic infection
Bibliographic record
Abstract
In the past 30 years, many amphibian species have suffered population declines throughout the world. Mass mortality have been frequently reported, and in several instances, infectious diseases appear to be the cause of death. The role that contaminants could play in these die-offs through immunotoxic effects has been poorly investigated. In this study, juvenile leopard frogs (Rana pipiens) were exposed for 21 d to a mixture of six pesticides (atrazine, metribuzin, aldicarb, endosulfane, lindane, and dieldrin) and subsequently challenged with a parasitic nematode, Rhabdias ranae. Exposure to the mixture at environmentally realistic concentrations significantly reduced lymphocyte proliferation. Three weeks after the end of the exposure, lymphocyte proliferation had recovered and was stimulated in frogs challenged with parasites with the exception of those previously exposed to the highest concentration. No pesticide effects on phagocytosis and splenocyte numbers were detectable at the end of the exposure period, but these two parameters were diminished 21 d after the infection challenge in frogs previously exposed to the highest levels of pesticides. In these animals, the prevalence of lung infection by R. ranae also tended to be higher. These results suggest that agricultural pesticides can alter the immune response of frogs and affect their ability to deal with parasitic infection.
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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".