Analysis of grasshopper (Orthoptera: Acrididae) population surveys in Saskatchewan: 1972–2004
Bibliographic record
Abstract
Abstract Grasshopper population forecasting and monitoring methods in western Canada have traditionally been linked to pest control decisions primarily by providing warnings of the likelihood of crop damage. Risk to crops is categorized as very light (1–4 grasshoppers/m2), light (>4–8 grasshoppers/m2), moderate (>8–12 grasshoppers/m2), severe (>12–24 grasshoppers/m2), or very severe (>24 grasshoppers/m2). A summary of grasshopper infestation by risk category indicated that in 29 of 33 years, infestations warranted ratings of severe or very severe, with the majority of these infestations occurring in the 1980s. Infestations involving the two categories severe and very severe encompassed about 84 900 and 85 600 km2 of agricultural land during the two largest infestations in 1985 and 2002, respectively. Keeping grasshopper populations below economic thresholds through preventative measures is the goal of integrated pest management. To determine when control measures are warranted, producers are asked to monitor grasshopper populations and estimate the number of grasshoppers per square metre. Economic thresholds provide guidance in making a decision as to whether control is warranted in different crops. Using economic thresholds as a guide, this study identified the eco-districts most at risk of crop damage within each of four major eco-regions of Saskatchewan. Overall, risk was highest in five eco-districts of the Mixed Grassland Eco-region. These findings provide guidance for the agriculture industry in relation to grasshopper management and for future survey programs in relation to targeting regions of the province most at risk from grasshoppers.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".