Condition‐dependent nesting in bluegill sunfish <i>Lepomis macrochirus</i>
Bibliographic record
Abstract
1. There are two prominent, nonmutually exclusive hypotheses to explain the timing of reproduction in animals: energetic constraint and adaptive behaviour. 2. We tested these hypotheses by quantifying the costs and benefits of nesting at different times in the season for male bluegill sunfish Lepomis macrochirus, a species with paternal care, in Lake Opinicon (Ontario, Canada). 3. The value of nesting at different times during the breeding season (RSb) was determined from spawning individuals as RSb = Pb x Cb x Sb x Ob, where Pb is the probability of spawning during each bout b, Cb is the expected brood size, Sb is the expected brood survivorship to 'swim-up', and Ob is the survivorship of free-swimming fry to age 1 year. 4. The results show that the value of nesting peaks during the middle of the season. However, nesting patterns varied with male condition and not all males nested at the peak. 5. Larger males, which were able to nest multiple times, first nested early in the season when overwinter survivorship of offspring and renesting opportunities later in the season were maximized. These males had the highest seasonal reproductive success. 6. Smaller males, which nested a single time, delayed nesting until the middle of the season when spawning opportunities and brood sizes were greatest. 7. These data suggest that both energetics and adaptive behaviour play roles in determining the timing of reproduction.
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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".