Abundance indices and biological traits of juvenile salmon (Salmo salar) sampled in three rivers on the Atlantic and Channel coasts (France)
Bibliographic record
Abstract
BACKGROUND: ) is an anadromous migratory species adapted to cool temperatures. It is protected by the Bern convention and by the European Habitats Directive. It has been listed as vulnerable by the French IUCN Red List. Salmon decline is the result of combined and cumulated, mainly anthropic, causes: climate change, increasingly high number of impoundments, degradation of water quality and habitat and over-exploitation by fisheries. Monitoring of this species has been carried out on three rivers in France (Southern part of the distribution area) to produce data and knowledge (growth, precocious maturity, survival) for stock management.For 24 years, a specific and standardised electric fishing protocol has been used to target young-of-the-year (0+ parr) Atlantic salmon. Sampling was restricted to areas with shallow running water that flows over a coarse bottom substrate, i.e. the preferred habitat of young salmon. This monitoring and inventory of growing areas thus allows assessment of juvenile recruitment and provides baseline data required to calculate total allowable catches (TACs). NEW INFORMATION: The dataset currently consists of 47,077 occurrence data points from 105 sites spanning up to 24 years in three different watersheds in France. Beyond our project, this dataset has a clear utility to research since it associates abundance measurements with the measurement of biological traits and the collection of tissue samples. It allows for current and retrospective characterisation of individuals or populations, according to life history traits and genetic features in relation to changes in environmental conditions. The fact that the monitoring takes place in France, the southern part of the distribution area, over 24 years, makes the dataset particularly relevant for climate change studies.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".