Physiological maturity at a critical life‐history transition and flight ability at fledging
Bibliographic record
Abstract
Summary Developmental maturity (e.g. body condition, body mass) at major life‐history transitions is known to affect fitness across a wide range of taxa. Fledging (leaving the nest), a major life‐history transition in birds, is associated with high post‐fledging mortality and is widely assumed to be related to poor initial flight ability of fledglings, which, in turn, might be related to developmental maturity at fledging. We investigated individual variation in developmental maturity of both somatic and physiological traits at this critical life‐history transition in different ecological contexts (year, first or second broods) to determine the importance of physiological traits related to oxygen‐carrying capacity (haematocrit, haemoglobin) for individual variation in initial flight ability at fledging. Haemoglobin concentration and haematocrit at fledging had much higher variance than somatic traits and were more variable across ecological contexts. Furthermore, fledgling haemoglobin concentration was the least developmentally mature of all traits (on average, only 78% of adult concentration). Fledglings from second broods, which are known to have lower post‐fledging survival, were less developmentally mature than fledglings from first broods for all traits (except tarsus), with haematocrit and haemoglobin concentration being the most developmentally immature traits (in first vs. second broods, haematocrit: 47·1% vs. 40·9%; haemoglobin: 13·3 vs. 11·6 g dL−1). Models predicting individual variation in two aspects of initial flight ability (total energy gain, take‐off angle) were significantly improved when physiological traits (in particular haemoglobin) were incorporated into models based on somatic traits. A lay summary is available for this article.
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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) | 0.002 | 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".