Contrasting trends between species and catchments in diadromous fish counts over the last 30 years in France
Bibliographic record
Abstract
The decline and collapse of populations have been reported for a large range of taxa. Diadromous fishes migrate between fresh water and the sea and encounter many anthropogenic pressures during their complex life cycle. In spite of being of ecological, cultural and economic interest, diadromous fishes have been in decline for decades in many parts across the world. In this study, we investigated the change in five diadromous fish counts in France over a 30-year period using 43 monitoring stations located in 29 rivers across 18 catchments. Our hypothesis was that the counts of these species evolved in a contrasting way between catchments. We also tested the effect of five drivers potentially contributing to the observed trends: catchment, latitude, presence of commercial fisheries, improvement of ecological continuity and salmon stocking. We found contrasting trends in fish counts between species at the national scale, with some taxa increasing (Anguilla anguillaandSalmo trutta), some showing a slight increase (Salmo salar) and some decreasing (Alosaspp. andPetromyzon marinus). For each taxon, exceptAnguilla anguilla, we highlighted a significant catchment effect indicating contrasting trends between catchments and stations. However, we found no significant effect of catchment characteristics for any of the studied taxa.
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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".