Assessing the climatic potential for epizootics of the gypsy moth fungal pathogen <i>Entomophaga maimaiga</i> in the North Central United States
Bibliographic record
Abstract
The fungal pathogen Entomophaga maimaiga Humber, Shimazu et Soper has become an important biocontrol for gypsy moth ( Lymantria dispar (L.)) in the northeastern United States and is commonly introduced into new areas with established gypsy moth populations. Germination of the fungus is dependent on spring temperature and moisture, but specific conditions associated with epizootics have not been determined. Whether E. maimaiga will be as effective in other regions that experience different weather conditions is not yet known. We examined similarity of weather conditions associated with 16 documented E. maimaiga epizootics with conditions at 1351 North American locations using the climate-matching software CLIMEX. Based on CLIMEX’s overall index of climatic similarity, long-term annual climatic patterns across much of the eastern United States were 60%–80% similar to the conditions associated with epizootics. Monthly weather records from 1971 to 2000 in nine North Central states were examined to compare precipitation and temperature with conditions observed during epizootics. Based on climatic averages identified with the documented epizootics, temperature and precipitation conditions in Illinois, Indiana, Iowa, Kentucky, Missouri, and Ohio were more conducive for epizootics than conditions in Minnesota, Wisconsin, and Michigan, which were likely to support E. maimaiga epizootics in fewer than 6 of the 30 years considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".