Juvenile Nutritional Stress Affects Growth Rate, Adult Organ Mass, and Innate Immune Function in Zebra Finches (<i>Taeniopygia guttata</i>)
Bibliographic record
Abstract
Developmental conditions may influence many aspects of adult phenotype, including growth and immune function. Whether poor developmental environments impair both growth and immune function or induce a trade-off between the two processes is inconclusive, and the impact of the timing of stress in determining this relationship has so far been overlooked. We tested the hypothesis that the long-term effects of nutritional stress on growth, body composition, and immune function in zebra finches (Taeniopygia guttata) are different depending on whether stress is experienced during an early or a juvenile phase (i.e., before or after nutritional independence, respectively). We raised birds on high (H) or low (L) food conditions until posthatch day (PHD) 35 and switched treatments for half of the birds in each of the H and L groups from PHD 36 to 61. We found that unfavorable juvenile conditions (PHD 36-61) increased somatic growth rates and liver mass, body fat, and some aspects of immune function. We also observed a positive relationship between growth and immune function, as individuals that grew faster as juveniles also had better innate immune responses as adults. There was no effect of treatment on basal metabolic rate. These findings demonstrate the importance of juvenile developmental conditions in shaping multiple aspects of the adult phenotype.
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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".