The use of modelling approaches to explore interactions in two aquatic host-pest systems
Bibliographic record
Abstract
Modelling is a useful tool that has been applied in both human and animal epidemiological research. A model is a simplified system that represents a much more complex phenomenon. Various types of models are available. They are generally used for the purposes of explaining phenomena, making predictions, or exploring different scenarios. Several challenges have been encountered during the construction of models in aquatic animal health and are discussed in the dissertation. The research documented in this dissertation aimed to demonstrate the application of modelling to address specific health and production issues associated with two aquatic animal species (blue mussels and wild Pacific salmon).\nThe first problem dealt with sea lice infestations in wild Pacific salmon populations on the west coast of British Columbia, Canada. The levels of sea lice infestations on wild chum and pink salmon were described and factors associated with inter-annual variation of the infestations were identified using a multivariable logistic regression model. This model included site information as a random effect, to account for spatial aggregation, which provided further details on the degree of clustering at the site level and suggested that the infestation levels depended on the location of fish. This raised the question as to where the risks were and, as a result, a spatial cluster analysis technique (i.e. spatial scan statistics) was used to identify when and where the clusters (of elevated sea lice infestation levels) occurred. The results from clustering analysis can facilitate the hypothesis-generating process for future studies. The second issue was the problem of mussel loss due to biofouling by tunicates (Ciona intestinalis) on Prince Edward Island mussel farms, which was assessed through the use of a mathematical model to describe the dynamics of C. intestinalis populations over the growing season. The model incorporated temperature dependencies, which allowed for the assessment of population dynamics under different temperatures, and was then used to evaluate the effectiveness of different mitigation strategies, using fewer resources than would be required if field trials were undertaken.\nThe research documented in this dissertation demonstrates the use of modelling to address production and health issues in the context of aquatic animals. In addition to the use of field-based trials the research also suggests that modelling can be used as an alternative method to investigate various scenarios and facilitate management planning with advantages in time and cost savings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".