Inter‐river, ‐annual and ‐seasonal variability in fecundity of Atlantic salmon, <i>Salmo salar</i> L., in rivers in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Abstract Fecundity is an integral component of the calculation of Atlantic salmon, Salmo salar L., egg depositions in rivers. Fecundity determinations can be time consuming and prohibitively expensive in terms of application on a broad scale. Consequently, where river specific and annual data are not available, default means are used in calculations in Newfoundland and Labrador. It is important therefore to know the extent of variability among rivers, years and seasons and the potential error involved in using default values. Annual fecundity data were available for one river in Labrador and nine rivers in Newfoundland. Fecundity was determined from ovaries collected in the recreational fishery in the summer for all 10 rivers. For three of these rivers, fecundity determined from summer sampling was compared with that obtained from sampling at time of spawning in autumn. There was significant variability in fecundity with length as a covariate among rivers, years and seasons. Mean number of eggs per female decreased between 8.3% and 29.0% from summer to autumn while mean number of eggs per cm decreased from 5.0% to 28.5%. Depending on the measure of relative fecundity used (no. of eggs kg−1 or no. of eggs cm−1), results of simulations showed that estimates of egg deposition incorporating defaults can deviate from those obtained by applying year‐specific and river‐specific values by 50–75%, without adjusting for the seasonal reduction in fecundity, and by 30–50% with an adjustment. A sensitivity analysis revealed that of three parameters used in the calculation of egg deposition (size, percent female and fecundity), fecundity was the most influential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".