Understanding the ecology of disease in Great Lakes fish populations
Bibliographic record
Abstract
Disease may be an important factor affecting wild fish population dynamics in the Great Lakes, but a lack of information on the ecology of fish disease currently precludes the prediction of risks to fish populations. Here we propose a conceptual framework for conducting ecologically-oriented fish health research that addresses the inter-relationships among fish health, fish populations, and ecosystem dysfunction in the Great Lakes. The conceptual framework describes potential ways in which disease processes and the population-level impacts of disease may relate to ecosystem function, and suggests that functional ecosystems are more likely to be resilient with respect to disease events than dysfunctional ecosystems. We suggest that ecosystem- or population-level research on the ecology of fish disease is necessary to understand the relationships between ecosystem function and fish health, and to improve prediction of population-level effects of diseases on wild fish populations in the Great Lakes. Examples of how the framework can be used to generate research questions are provided using three disease models of current interest in the Great Lakes: thiamine deficiency complex, botulism, and bacterial kidney disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".