No Trade-Offs between Lipid Stores and Structural Growth in Juvenile Zebra Finches Undergoing Nutritional Stress during Development
Bibliographic record
Abstract
Nutritional conditions during development can affect both structural growth and body fat deposition. Body size and body fat each have significant consequences for fitness, yet few studies have investigated how young birds balance resource allocation between structural growth and fat reserves. We raised zebra finches (Taeniopygia guttata) in consistently high- or low-food conditions until posthatch day 35 (PHD 35). From this age until PHD 62, half of the birds in each condition were switched to the other treatment, while the rest were maintained on the same conditions. Body mass, lean mass, body fat, and tarsus length were measured before (PHD 25) and after (PHD 55) nutritional independence. Precise measures of body composition were obtained noninvasively at both ages using quantitative magnetic resonance analysis. At PHD 25, birds in the high treatment had more body mass and lean mass than birds in the low treatment, but nutritional treatments did not affect body fat at this age. Unexpectedly, the strategic response of birds that experienced deteriorating food availability was to maintain body mass by increasing body fat and decreasing lean mass. Birds that experienced an improvement in food availability significantly increased body mass by increasing lean mass and not body fat. Birds maintained on a low diet throughout did not significantly increase body mass, lean mass, or body fat. Tarsus length was not affected by nutritional manipulations. These findings indicate that nutritional stress did not affect the relationship between skeletal growth and body fat deposition because lean mass, body fat, and tarsus length can be independently regulated at different developmental periods depending on nutritional conditions.
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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 | 0.001 |
| 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".