Long-term changes in body condition and gillnet selectivity in Lake Constance pelagic spawning whitefish (<i>Coregonus wartmanni</i>)
Bibliographic record
Abstract
The body condition of Lake Constance pelagic spawning whitefish (Coregonus wartmanni) has changed substantially during the past century and altered the length-based selectivity of gillnets. Linked hierarchical models using Bayesian inference and error propagation were used to estimate the monthly body condition of whitefish from 1932 to 2018 and condition-dependent gillnet selectivity from 1964 to 2018. As expected, body condition followed past trends in nutrient dynamics and was highest in summer months. Body condition was clearly linked to gillnet selectivity, with a weight increase of a 300 mm whitefish from 205 to 260 g predicted to reduce the mean (from 374 to 330 mm) and standard deviation (from 30.8 to 25.1 mm) of lengths selected by a 38 mm mesh gillnet. Simulations demonstrate that such changes can reduce the mean age in harvest by over 1 year and greatly bias population age distribution estimates if selectivity changes are ignored. Similar variation in gillnet selectivity is expected where trophic conditions or other factors cause body condition differences, and accounting for these changes could reduce biases to inform fishery management.
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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 | 0.002 |
| 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) | 0.001 | 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".