European eel (<i>Anguilla anguilla</i>): prediction of spawner escapement from continental population parameters
Bibliographic record
Abstract
This paper describes the assessment of silver European eel (Anguilla anguilla) escapement based on a "sedentary" population fraction analysis in a 60-km2 watershed of northern Brittany (France). Downstream migration fluxes were monitored using eel traps and related to environmental factors. Intensive electrofishing and fyke-net fishing were conducted to assess eel biomass, densities, and population structure. A total of 564 eels, including 81 silver eels, were PIT tagged. In 1996, 616 eels were caught in the catchment including 68 silver eels (11%). During the following downstream migration period, 12 of the PIT-tagged silver eels, among a total of 678, were recaptured in the downstream traps. Seven were recaptured in the catchment in 1997. It was shown that (i) only about 20% of the silver eels present in the catchment emigrated during the following year, (ii) 12% stayed in the catchment including two (3.4%) that recovered yellow eel characteristics, and (iii) the other eels either died or settled in the catchment but were not recaptured. It was also estimated that 2% (650 eels) of the population (34 000 eels) among 3000 silver eels considered "emigration candidates" emigrated each year,.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".