New insights into the collapse of Upper Lake Constance whitefish (<i>Coregonus wartmanni</i>) and the impact of commercial fisheries management
Bibliographic record
Abstract
The whitefish Coregonus wartmanni is the key fishery resource in Upper Lake Constance (ULC), one of Central Europe’s largest lakes. A significant stock decline resulted in the closure of the commercial whitefish fishery in 2024. Reasons for the decline have been contested, with suggestions ranging from environmental changes to overfishing. As in many inland fisheries, management in ULC previously lacked standardized protocols for stock assessment, and relied instead on technical measures like regulating mesh size and net numbers. To assess stock dynamics and estimate biomass and fishing mortality over the past 25 years (1997–2022), a surplus production model was applied using scientific gillnet surveys and commercial catch data. The results confirm that the whitefish stock is at a historically low level. Apparently, in 2012, when the stock already showed signs of overfishing, invasion of the pelagial by non-native stickleback ( Gasterosteus aculeatus) triggered an ecosystem shift. Additional factors including oligotrophication, other invasive species and climate change also impact stock development, suggesting that reduced fishing pressure alone may not be enough to ensure short-term stock recovery.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".