Predator–prey dynamics mediate long-term production trends of cisco (<i>Coregonus artedi</i>) in a northern Wisconsin lake
Bibliographic record
Abstract
We quantified production, biomass, and production to biomass (P/B) ratios for cisco (Coregonus artedi) in Trout Lake, Wisconsin, USA (2001–2015). Across all years, annual production, biomass, and P/B were variable ranging 0.6–30.2 kg·ha−1·year−1, 1.2–39.7 kg·ha−1, and 0.4–0.9·year−1, respectively. Cisco production exhibited obvious decline. However, neither biomass nor P/B changed significantly over time. Long-term patterns of environmental conditions remained unchanged during the study and were unrelated to cisco production. However, lake trout (Salvelinus namaycush) relative abundance showed a strong inverse relationship with cisco production and biomass. Intense lake trout stocking has occurred in this lake over time to conserve a genetically unique strain of the species. These management efforts may have had the unintended consequence of amplifying top-down predation on cisco. Since cisco P/B has gone largely unchanged, cisco production would be predicted to rebound quickly to adaptive reductions in lake trout stocking. Further increases in lake trout numbers could place both populations at risk of collapse. This study provides an example of a fisheries production approach for understanding and conserving cold-water fisheries, especially in lakes dominated by ciscoes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".