Supplementary material from "Population density and timing of breeding mediate effects of early life conditions on recruitment"
Bibliographic record
Abstract
Identifying the factors driving juvenile recruitment is crucial for predicting the response of populations to environmental change. Importantly, how early life conditions carry over to influence recruitment may be highly dependent on the context in which they occur. For example, the effects of challenging early-life conditions may be more pronounced under high densities or when young are born late in the season. We examined the ecological factors influencing local recruitment spanning three decades in Savannah sparrows (Passerculus sandwichensis) breeding on Kent Island, NB, Canada. The effect of nestling mass on recruitment depended on both population density and fledging date. At low population densities or early in the breeding season, nestling mass had little effect on recruitment probability. At high population densities or later in the breeding season, mass had a stronger effect, with heavier individuals more likely to recruit. Lighter fledglings may have lower recruitment under challenging conditions due to lower competitive ability, lower mobility, and greater susceptibility to resource limitation relative to heavier fledglings. Our findings have important implications for life history evolution and selection on body size in a changing world, highlighting the relationships between population density, timing of breeding, and offspring recruitment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.917 | 0.539 |
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".