Density‐dependent growth of young‐of‐the‐year Atlantic salmon (<i>Salmo salar</i>) revisited
Bibliographic record
Abstract
Imre I, Grant JWA, Cunjak RA. Density‐dependent growth of young‐of‐the‐year Atlantic salmon (Salmo salar) revisited. Ecology of Freshwater Fish 2010: 19: 1–6. © 2009 John Wiley & Sons A/S Abstract – The length of individual young‐of‐the‐year (YOY) Atlantic salmon (Salmo salar) in Catamaran Brook decreases with increasing population density following a negative power curve. Because most of this decrease in growth rate occurs at low densities (<1 fish·m−2), ( Imre et al. 2005 ; Journal of Animal Ecology, 74: 508–516)suggestedthat exploitation competition for drifting prey rather than space limitation might be responsible for this pattern. Recently, ( Ward et al. 2007 ; Journal of Animal Ecology, 76: 135–138) showed that the negative power curve of growth rate versus densitycanbe caused by other mechanisms and suggested that Imre et al.’s evidence for density‐dependent growth would have been stronger if we had analysed final size versus initial density rather than final density. We examined (i) whether the negative power curve of size versus density was also apparent in an analysis of final size versus initial density and tested two predictions that emerge from Ward et al.’s model, (ii) the variance in body size increases with population density, and (iii) the maximum fish size at a site is density‐independent. The final size of YOY salmon decreased with increasing initial density following a negative power curve. Our data did not provide strong support for the above predictions emerging from Ward et al.’s model. Our analyses of different years, sites and seasons were consistent with the hypothesis of density‐dependent growth of YOY salmon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".