Common paths link food abundance and ectoparasite loads to physiological performance and recruitment in nestling blue tits
Bibliographic record
Abstract
1 Identifying how selection shapes life-history traits by causally relating environment to phenotype, performance and fitness has often proven elusive due to limitations of classical analysis methods, which only identify covariance in traits, and to the difficulties in experimentally manipulating environment to expose cause and effect in wild populations. 2 In an approach resembling the experimental method common to all modern research, structural equation modelling can not only identify covariance in traits, but also test hypotheses of direct and indirect causal paths that tie environment to phenotypes and fitness through natural selection. Here, we use novel confirmatory path analyses and a long-term study of Corsican blue tits to analyse the interactions between environmental variables (prey abundance and ectoparasite load), phenotypic traits (mass and haematocrit), physiological performance (aerobic capacity) and nestling recruitment. 3 Our analyses show that an antagonistic interaction between ectoparasites and food abundance sets tissue development and oxygen carrying capacity of blood at fledging, and that identical paths link these variables to physiological performance and recruitment. 4 This study suggests that metabolic capacity at fledging may be important in determining subsequent recruitment and unmasks subtle fitness costs of an ectoparasite.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".