Lakewide Estimates of Alewife Biomass and Chinook Salmon Abundance and Consumption in Lake Ontario, 1989–2005: Implications for Prey Fish Sustainability
Bibliographic record
Abstract
Abstract Stocking levels of Chinook salmon Oncorhynchus tshawytscha for Lake Ontario have been highly controversial since the early 1990s, largely because of uncertainties about lakewide abundance and rates of prey consumption. Previous estimates have focused on years before 1995; since then, however, the Lake Ontario ecosystem has undergone substantial changes, and there is new evidence of extensive natural recruitment. Presented here are new abundance estimates of Chinook salmon and alewives Alosa pseudoharengus in Lake Ontario and a reevaluation of the potential risk of alewife population collapse. We found that Lake Ontario has been supporting, on average (1989–2005), 1.83 × 106 (range, 1.08 × 106 to 3.24 × 106) Chinook salmon of ages 1–4, amounting to a mean annual biomass of 11.33 × 103 metric tons (range, 5.83 × 103 to 23.04 × 103 metric tons). During the same period (1989–2005), the lake supported an alewife biomass of 173.66 × 103 metric tons (range, 62.37 × 103 to 345.49 × 103 metric tons); Chinook salmon of ages 1–4 consumed, on average, 22% (range, 11–44%) of the alewife biomass annually. Because our estimates probably underestimate total consumption and because Chinook salmon are only one of several salmonine species that depend on alewives, predation pressure on the Lake Ontario alewife population may be high enough to raise concerns about long‐term stability of this predator–prey system.
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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 | 0.001 | 0.002 |
| 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".