Bioclimatic model of Melanoplus sanguinipes (Fabricius) (Orthoptera: Acrididae) populations in Canada and the potential impacts of climate change
Bibliographic record
Abstract
The northern Great Plains of North America has experienced a number of severe grasshopper infestations over the last 100 y. Grasshopper densities have been observed to be in synchrony over vast geographical areas. Weather is probably the most significant factor that affects fluctuations in population abundance of Melanoplus sanguinipes, the migratory grasshopper. Inferential modeling was used to develop a bioclimatic model of M. sanguinipes that closely agreed with current distribution and abundance patterns of this species in North America over a 30-y period. Incremental climate change scenarios were then applied to the bioclimatic model to predict changes to potential distribution and relative abundance of M. sanguinipes, resulting from climate change. Compared to predicted range and distribution under current climate conditions, model results indicated that M. sanguinipes would have increased range and relative abundance for temperature increases between 1 and 7°C. The model predicted that the range of this crop pest would be extended to regions that are not currently used for agricultural production in North America.
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 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".