Insect pest complexes associated with wheat and canola crops in the Canadian Prairies Ecozone: pest risk in response to variable climates using bioclimatic models
Bibliographic record
Abstract
Abstract Wheat, Triticum aestivum Linnaeus (Poaceae), and canola, Brassica napus Linnaeus (Brassicaceae), yield is at risk from insects, weeds, and pathogens. Insects must adapt to both seasonal and annual weather patterns and are known to respond to climate with changes in their distribution and relative abundance. Subsequently, risk to crop production also changes. Models that account for multiple species can serve to assess risk and address those risks proactively by monitoring, detecting, and managing insect pests. Bioclimatic models, developed individually for nine insect pests, were used to create a model to estimate risk to canola and wheat crops associated with the activity of multiple pest species. Once developed, the multiple-species model was used to analyse how crop risk responds to variation in temperature and precipitation across the prairies. For this analysis, we compared insect response and subsequent risk (a measure of the number of co-occurring pest species) to canola and wheat in current climate conditions and six incremental scenarios (warmer, cooler, drier, wetter, cooler and wetter, and warmer and drier). Results of the multiple-species model predict how pest complexes respond to climate conditions. The model will help increase risk awareness associated with insect pests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".