Physiological predictors of reproductive performance in the European Starling ( <i>Sturnus vulgaris</i> ): II. Multivariate Analysis
Bibliographic record
Abstract
Abstract Physiological variation is generally thought or supposed to underlie variation in fitness related traits in wild animals, including reproductive effort, reproductive success, and survival. However, physiological markers of individual quality have proven elusive. In this paper and its companion, we use data on 14 physiological parameters measured in 152 observations of 93 individual European starlings ( Sturnus vulgaris ) over 2 years in an attempt to understand how physiological variation relates to variation in current breeding productivity, future fecundity, and survival. The companion paper, focusing on univariate analysis, showed that individual physiological parameters have little relationship with these performance measures. Here, we used more sophisticated statistical approaches in an attempt to extract a multivariate signal from the biomarkers – physiological dysregulation as calculated via statistical distance, and a number of principal components analysis approaches. Broadly speaking, there was a surprising lack of association between physiology and performance: while some physiological summary measures were associated with some performance measures, the associations were not particularly strong or robust given the large number of statistical tests conducted. This implies either that there are relatively few links between physiology and performance, or, more likely, that the complexity of these relationships exceeds our ability to measure and model it, even using state-of-the-art statistical approaches. This is likely particularly true because our population was quite heterogeneous; we nonetheless urge caution regarding the over-interpretation of isolated significant findings in the literature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".