Modeling shell disease in American lobster (<i>Homarus americanus</i>) as individual-based health trajectories
Bibliographic record
Abstract
The emergence of epizootic shell disease in American lobsters (Homarus americanus) has presented many new challenges to understanding the interface between disease and the management of the lobster fishery. While a variety of the potentially causative and correlative factors for shell disease have been explored, a clear etiological agent remains elusive. The recency of this disease and the lack of identifiable causal agents have hindered the development of conceptual models that can yield testable predictions. Here, a model originally developed for human–parasite interactions was applied to lobster shell disease as a means to unify the broad experimental and field observations. The model is a graphical means to understand the onset and severity of shell disease and is a function of the length of the molt cycle and the rate of the decrease of health both before and after lesion formation as a function of bacterial abundance and pathogenicity. The model also accounts for shell hardening and passive and active portals of entry for the bacteria. The timing for a conceptual understanding of the epidemiology of shell disease is critical because its prevalence is increasing in key fishing areas. Ideally, such a model will help researchers create hypothesis-driven predictive experiments from which we can further our understanding of an important disease to a critical member of the Gulf of Maine ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".