The relative influence of breeding competition and habitat quality on female reproductive success in lacustrine brook trout (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Egg losses for female salmonines primarily occur through competition for egg incubation sites (i.e., redds) and the differences in quality among these sites. Through detailed observations and an experiment linking egg survival to groundwater flow, we estimated the relative influence of redd superimposition and habitat quality on female reproductive success for a population of lake-spawning brook trout (Salvelinus fontinalis). Three quarters of all spawning sites were reused by multiple females; however, brood loss was much less (28%38%) because large females spawned earlier and constructed deeper nests. The relationship between groundwater flow rate and egg survival was not linear, with consistent egg survival occurring only at sites with flows over 20 mL·m2·min1. Varying scenarios of redd superimposition and habitat-related egg survival resulted in an estimated 4%-21% of deposited eggs surviving to emergence and greatly reduced the size-related advantages of larger females owing to fecundity. Limited numbers of high-quality spawning sites and overall low survival of eggs resulted in habitat being the dominant route of egg loss. In the absence of female competition, spawning habitat alone accounted for egg losses of 67%91% and points to the importance of physical habitat features in the maintenance of brook trout populations.
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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".