Disease ecology of wild fish: opportunities and challenges for linking infection metrics with behaviour, condition, and survival
Bibliographic record
Abstract
Surmounting evidence supports that infectious agents play a critical role in shaping fish physiology, behaviour, and survival. The exclusion of disease-causing agents from fisheries research has resulted in major knowledge gaps that may limit the predictive capacity of ecological models. A major barrier in wild fisheries epidemiology is the logistical constraints associated with observing disease and obtaining samples from free-ranging fish, restricting the vast majority of research to laboratory studies or aquaculture facilities. For fisheries ecologists, including infectious agents can provide greater insight into observed phenomena, particularly with respect to fish physiology (e.g., metabolism), movement (e.g., migration rates), behaviour (e.g., habitat selection), personality (e.g., bold versus shy), and survival. Here we provide a brief introduction to the current understanding of disease ecology in wild fish and describe technological advances in both epidemiology and fisheries and aquatic sciences that can be used in tandem to create comprehensive studies of disease ecology in wild fishes. Combining nonlethal sampling and molecular genetic-based identification methods with field studies creates vast opportunities for innovative study designs that have the potential to address the true complexity of aquatic ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".