Risk Reduction of an Invasive Insect by Targeting Surveillance Efforts with the Assistance of a Phenology Model and International Maritime Shipping Routes and Schedules
Bibliographic record
Abstract
Reducing the risk of introduction to North America of the invasive Asian gypsy moth (Lymantria dispar asiatica Vnukovskij and L. d. japonica [Motschulsky]) on international maritime vessels involves two tactics: (1) vessels that wish to arrive in Canada or the United States and have visited any Asian port that is subject to regulation during designated times must obtain a predeparture inspection certificate from an approved entity; and (2) vessels with a certificate may be subjected to an additional inspection upon arrival. A decision support tool is described here with which the allocation of inspection resources at North American ports can be partitioned among multiple vessels according to estimates of the potential onboard Asian gypsy moth population and estimates of the onboard larval emergence pattern. The decision support tool assumes that port inspection is uniformly imperfect at the Asian ports and that each visit to a regulated port has potential for the vessel to be contaminated with gypsy moth egg masses. The decision support tool uses a multigenerational phenology model to estimate the potential onboard population of egg masses by calculating the temporal intersection between the dates of port visits to regulated ports and the simulated oviposition pattern in each port. The phenological development of the onboard population is simulated each day of the vessel log until the vessel arrives at the port being protected from introduction. Multiple independent simulations are used to create a probability distribution of the size and timing of larval emergence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".