Spatio-Temporal Model for Predicting Spring Hatch of the Spotted Lanternfly (Hemiptera: Fulgoridae)
Bibliographic record
Abstract
The effect of temperature on the rate of spotted lanternfly, Lycorma delicatula (White) (Hemiptera: Fulgoridae), egg development was investigated for a population in Pennsylvania. Mean developmental duration (days ± SE) for egg hatch was evaluated at five constant temperatures of 19.9, 24.2, 25.1, 26.7, and 30°C using egg masses laid during the fall of 2018 and collected in 2019 from Berks Co., Pennsylvania. Base temperature thresholds for egg development were estimated using intercept and slope parameters by fitting a linear relationship between average temperature and developmental rate for the Pennsylvania study, two Korean studies, and the combined data sets. The base threshold estimates were then used to calculate seasonal accumulated degree-days (ADD) and construct logistic equations for predicting cumulative proportion of hatch in the spring. The fitted logistic prediction equations were then graphed against the egg hatch observations from field sites in Pennsylvania (2017) and Virginia (2019). When base temperature estimates from the three studies and combined studies were used to calculate ADD, the logistic models predicted similar timing for seasonal egg hatch. Because the slopes and intercepts for these four data sets were not statistically different, a base temperature threshold of 10.4°C derived from the combined model is a good estimate for computing ADD to predict spotted lanternfly spring emergence across a spatio-temporal scale. The combined model was linked with open source weather database and mapping programs to provide spatiotemporal prediction maps to aid pest surveillance and management efforts for spotted lanternfly.
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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) | 0.001 | 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".