Inferring Adult Status and Trends from Juvenile Density Data for Atlantic Salmon
Bibliographic record
Abstract
Abstract Typically, juvenile survey data are not used explicitly to determine status, trends, or abundance designations for Atlantic salmon Salmo salar, even though they can be the only source of information for many populations. To determine whether juvenile data can be informative about adult abundance and status in Atlantic salmon, we evaluated the similarities in trends among age-classes for two data-rich populations using a nested log-linear model. We found relatively consistent and significant trends for the age-0, adult and egg time series, but the trends in juvenile density data for older age-classes were less consistent with adult abundance trends. A threshold-based analysis demonstrated that relatively low misclassification rates for adult status relative to a set reference level could be obtained from juvenile density estimates. Together, these results suggest that juvenile density data can be an informative proxy for adult abundance and may be useful as an indicator for large changes in population status relative to reference points. This would make data collection via electrofishing an appropriate monitoring method for fisheries management or conservation programs. However, the validity of the idea that dramatic changes in adult abundance will be mirrored in juvenile data partially depends on the specific age-classes monitored, the survey design, and the timing of density dependence in the population. Using juvenile data as an index would necessitate some prior knowledge of the underlying population dynamics before the method could be applied more generally. Received May 9, 2012; accepted August 8, 2012
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".