Individually variable energy management during egg production is repeatable across breeding attempts
Bibliographic record
Abstract
It is axiomatic that whole-animal metabolism, measured for example as daily energy expenditure (DEE), plays a central role in determining reproductive success and survival (fitness) in all organisms. Nevertheless, strong evidence for consistent systematic relationships between DEE and either individual traits (age, sex, body size), environmental factors (e.g. food availability, temperature) or 'fitness' traits (e.g. number of offspring, survival) remains far from compelling in birds and mammals. Recently, we suggested that female birds might utilise complex, individually variable energy management strategies to meet the metabolic demands of reproduction, generating a wide spectrum of effects on reproductive DEE, from overcompensation (net decrease in DEE) to additive effects (net increase in DEE). Here we show that this individually variable adjustment or 'plasticity' in energy expenditure associated with egg production is repeatable among individuals between successive breeding attempts in female zebra finches (Taeniopygia guttata). Our study highlights the importance (a) of measuring 'plasticity' or change associated with transitions of physiological state (e.g. non-breeding to breeding) based on multiple measurements of the same individual, and (b) of extending consideration of how selection might drive the evolution of phenotypic plasticity per se to include physiological and metabolic traits.
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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 | 0.001 |
| 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".