Modeling Ecological Dynamics of a Major Agricultural Pest Insect (<i>Melanoplus sanguinipes</i>; Orthoptera: Acrididae): A Cohort-Based Approach Incorporating the Effects of Weather on Grasshopper Development and Abundance
Bibliographic record
Abstract
Grasshoppers have been a chronic problem for agriculture on the Canadian prairies, the northern limits of the Northern Great Plains, since settlement of the region in the mid-1800s. Grasshopper pest management tools include biological control, cultural control, and insecticides. This article describes a mechanistic, or process-based, modeling approach used to assess the development and abundance of the migratory grasshopper, Melanoplus sanguinipes (Fabricius), based on a complex of environmental drivers. The purpose of the study was to develop and validate a model (using extensive field data) to quantify the effects of interannual weather variation on M. sanguinipes development and abundance in Saskatchewan, Canada. Overall, the accuracy of model predictions improved for later instars and adults such that predictions regarding adult populations were highly similar to field-collected data. The model provides greater understanding of how M. sanguinipes oviposition is related not only to adult densities, but also to the first appearance of adults and to weather conditions during the oviposition period. The model output can be used to provide accurate within-season updates of the status of grasshopper populations in western Canada to optimize pest management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".