Failure of physiological metrics to predict dominance in juvenile Pacific salmon (<i>Oncorhynchus</i> spp.): habitat effects on the allometry of growth in dominance hierarchies<sup>1</sup>Order of authors represents their contribution to the manuscript.
Bibliographic record
Abstract
Territoriality is one of the best examples of interference competition and generally results in larger, dominant individuals gaining preferential access to food. However, the expectation of higher growth of dominant individuals among juvenile salmonids has received only mixed support. We used outdoor semi-natural stream channels stocked with varying sizes of young of the year juvenile salmonids under high and low food rations (i) to examine the mechanisms underlying variation in the benefits of dominance and (ii) to demonstrate that inconsistencies in the apparent benefits of dominance are a logical outcome of the allometry of growth and differential energy intake among fish of different size in a dominance hierarchy. Growth of dominants exceeded that of subordinates when food was abundant, but subordinates grew faster than dominants in low food treatments and when dominant fish increased in size and approached the capacity of their habitat. In general, size disparity within a dominance hierarchy may promote higher growth of subdominants because of the lower energetic requirements of smaller individuals, and the allometry of fish growth, relative to habitat capacity, can reverse the expected growth and condition of dominant and subordinate fish.
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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 | 0.001 | 0.003 |
| 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".