Managing populations after a disease outbreak: exploration of epidemiological consequences of managed host reintroduction following disease-driven host decline
Bibliographic record
Abstract
Abstract Disease outbreaks in wild populations around the globe can lead to widespread mortality within populations, where recovery of individuals can be rare. An example of this population is the sunflower sea star Pyc-nopodia helianthioides in the Northeastern Pacific coast. The populations of this species, as well as many other sea star populations, have experienced massive mortality events due to an unidentified disease called Sea Star Wasting Disease (SSWD). Pycnopodia play a key role in providing top-down control of kelp grazers in rocky reefs across the Northeastern Pacific coast. This, combined with the massive declines in kelp coverage observed during the 2015-2016 marine heat wave observed in the Northeastern Pacific, has sparked an interest in reintroducing Pycnopodia individuals in the coast to potentially assist in recovery of the populations. However, the epidemiological implications of reintroducing healthy sea stars into the wild populations is an under-explored question. This work explores this question using a dynamical population model of Pycnopodia . We use this model to estimate the impacts of reintroducing healthy individuals into a wild sea star population. This analysis will provide valuable information for managers interested in restoring Pycnopodia populations.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| 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".