Lake Whitefish and <i>Diporeia</i> spp. in the Great Lakes:An Overview
Bibliographic record
Abstract
Because of growing concern in the Great Lakes over declines in abundance and growth of lake whitefish (Coregonus clupeaformis) and declines in abundance of the benthic amphipod Diporeia spp., a workshop was held to examine past and current trends, to explore trophic links, and to discuss the latest research results and needs. The workshop was divided into sessions on the status of populations in each of the lakes, bioenergetics and trophic dynamics, and exploitation and management. Abundance, growth, and condition of whitefish populations in Lakes Superior and Erie are stable and within the range of historical means, but these variables are declining in Lakes Michigan and Ontario and parts of Lake Huron. The loss of Diporeia spp., a major food item of whitefish, has been a factor in observed declines, particularly in Lake Ontario, but density-dependent factors also likely played a role in Lakes Michigan and Huron. The loss of Diporeia spp. is temporally linked to the introduction and proliferation of dreissenid mussels, but a direct cause for the negative response of Diporeia spp. has not been established. Given changes in whitefish populations, age-structured models need to be re-evaluated. Other whitefish research needs to include a better understanding of what environmental conditions lead to strong year-classes, improved aging techniques, and better information on individual population (stock) structure. Further collaborations between assessment biologists and researchers studying the lower food web would enhance an understanding of links between trophic levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".