Validation of a sea lice dispersal model: principles from ecological agent-based models applied to aquatic epidemiology
Bibliographic record
Abstract
Sea lice are one of the most economically costly and ecologically concerning problems facing the salmon farming industry. Here, we validated a coupled biological and physical model that simulated sea lice larvae dispersal from salmon farms in the Broughton Archipelago (BA), British Columbia, Canada. We employed a concept from ecological agent-based modeling known as ‘pattern matching’, which identifies similar emergent properties in both the simulated and observed data to confirm that the simulation contained sufficient complexity to recreate the emergent properties of the system. One emergent property from the biophysical simulations was the existence of sub-networks of farms. These were also identified in the observed sea lice count data in this study using a space-time scan statistic (SaTScan) to identify significant spatio-temporal clusters of farms. Despite finding support for our simulation in the observed data, which consisted of over a decade’s worth of monthly sea lice abundance counts from salmon farms in the BA, the validation was not entirely straightforward. The complexities associated with validating this biophysical dispersal simulation highlight the need to further develop validation techniques for agent-based models in general, and biophysical simulations in particular, which often result in patchiness in their dispersal fields. The methods utilised in this validation could be adopted as a template for other epidemiological dispersal models, particularly those related to aquaculture, which typically have robust disease monitoring data collection plans in place.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".