Effect of testosterone on immunocompetence, parasite load, and metabolism in the common wall lizard (<i>Podarcis muralis</i>)
Bibliographic record
Abstract
Testosterone can benefit individual fitness by increasing ornament colour, aggressiveness, and sperm quality, but it can also impose both metabolic and immunological costs. However, evidence that testosterone causes immuno suppression in freely living populations is scant. We studied the effects of testosterone on one component of the immune system (i.e., the cell-mediated response to phytohaemagglutinin), parasite load, and metabolic rate in the common wall lizard, Podarcis muralis (Laurenti, 1768). For analyses of immunocompetence and parasitism, male lizards were implanted at the end of the breeding season with either empty or testosterone implants and were returned to their site of capture for 5–6 weeks before recapture. For analyses of the effects of testosterone on metabolic rate, male lizards were captured and implanted before hibernation and were held in the laboratory for 1 week prior to calorimetry. Experimental treatment with testosterone decreased the cell-mediated response to the T-cell mitogen phytohemagglutinin and increased mean metabolic rate. No effects of testosterone on the number of ectoparasites, hemoparasites, and resting metabolic rate could be detected. These results are discussed in the framework of the immunocompetence handicap hypothesis and the immuno-redistribution process hypothesis.
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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".