Using stable isotopes to assess the distribution of reproduction by migratory and resident Salmo trutta within river systems: some complicating factors
Bibliographic record
Abstract
Understanding the distribution of spawning resident and migratory trout in a river system is important for fishery management. In most cases this information is lacking or only partial, and mostly derived from surveys of breeding adults. Stable isotope analysis of fry provides an alternative method by which to determine the relative importance of different areas for migratory and resident fish breeding. The ratios of carbon and nitrogen stable isotopes in fish tissue vary predictably between offspring from different forms; sea trout typically show higher abundance of the heavier stable isotopes of both elements. These are passed to their offspring, allowing distinction at this phase. Here we show the results from applying this technique to two different river systems; the Gala Water tributary of the River Tweed and tributaries of the River Deveron. Although it is generally possible to distinguish migratory and resident offspring, interpretation is complicated by considerable isotopic variation within river systems. This may be linked to the influence of freshwater feeding, catchment-scale land-use patterns and isotopic variation in adults. In order to maximise the utility of stable isotope-based techniques in fisheries management, these factors need to be more fully understood.
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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 | 0.001 | 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) | 0.002 | 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".