Trade-offs between molt and immune activity in two populations of house sparrows (<i>Passer domesticus</i>)
Bibliographic record
Abstract
Molt and immune defense are critical activities in which all birds must invest. Because each is costly, wild passerines may have to decrease their investment in one activity if they are to increase investments to others. Here, I studied such moltimmune trade-offs in one neotropical and one north-temperate population of house sparrows (Passer domesticus (L., 1758)). I included two populations in my study to investigate if moltimmune trade-offs in this species are phenotypically plastic or fixed. I expected that if they were fixed, then immune activity, molt, and immunemolt trade-offs would be distinct between populations after they were kept in captivity for 1 year under similar conditions. I found evidence for moltimmune trade-offs in house sparrows. Feather growth was inversely related to cutaneous immune activity to phytohemagglutinin (PHA). Furthermore, feather growth 3 weeks post immune challenge was lower in immune-challenged birds relative to saline-injected controls. However, there was no effect of population of origin on these patterns, or the rate of molt or PHA response at this time of year in each population. Thus, while house sparrows probably do face trade-offs between molt and immune activity in the wild, any variation in these trade-offs between populations are likely plastic responses to different environments.
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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 | 0.001 |
| 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".