Response of alewife abundance to the bacterial kidney disease outbreak in the Chinook salmon population of Lake Michigan: importance of predation
Bibliographic record
Abstract
Prey fish abundance can be influenced by predation (top-down) and food limitation (bottom-up) effects. I characterized temporal trends in yearling and older (YAO) alewife (Alosa pseudoharengus) biomass density, as estimated by a long-term bottom trawl survey, in Lake Michigan during 1973–2019. Special attention was given to the bacterial kidney disease (BKD) outbreak in the population of Chinook salmon (Oncorhynchus tshawytscha), the predominant predator on alewives in the lake. YAO alewife biomass density exhibited a steep and significant decline during 1973–1985, but then partially rebounded with a significant increase between the 1983–1985 and 1986–2003 (BKD years) periods, followed by a significant decrease between the 1986–2003 and 2004–2006 periods. YAO alewife biomass density showed another significant decline during 2004–2019. The BKD outbreak led to a partial relaxation of predation on the YAO alewife population, and YAO alewife biomass density responded by showing a moderate increase in 1986 and then fluctuating about this moderately higher level for more than 15 years. Overall, temporal trends in YAO alewife biomass density were primarily driven by predation.
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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) | 0.001 | 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".