Population biology of infectious diseases shared by wild and farmed fish
Bibliographic record
Abstract
Global fisheries landings ceased increasing decades ago, causing an increasing shortfall in wild seafood supply and an expansion of aquaculture. The abundance of domesticated fishes now dwarfs related wild fishes in some coastal seas, changing the dynamics of their infectious diseases. Transport and trade of seafood, feed, eggs, and broodstock bring pathogens into new regions and into contact with naïve hosts. Density-dependent transmission creates threshold effects where disease can abruptly switch from endemic to epizootic dynamics. Hydrodynamics allow pathogens to disperse broadly, interconnecting farms into metapopulations of domesticated host fish in regions that also support related species of wild fish. Spillover and spillback dynamics of pathogen transmission between wild and farmed fish can create novel transmission pathways or bioamplify pathogen abundance, potentially depressing or endangering wild fish. Mortality from natural predator–prey interactions may be synergistic or compensatory with these increased infections. Domestic environments may favour the evolution of undesirable pathogen traits, such as virulence and drug resistance, leading to the emergence of strains that cause high mortality and (or) evade treatment. Overall, these changes to the dynamics of infectious disease in coastal seas impose new constraints on the sustainability of both wild and farmed fish.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".