We stand on guard for thee: A brief history of pest surveillance on the Canadian Prairies
Bibliographic record
Abstract
Crop production had dominated the Canadian Prairies for the past century, and has been constantly challenged by various pathogens, insects, and weeds. An effective biovigilance program to manage these crop pests requires continuous, timely, and detailed pest surveillance, to understand how pest populations are changing over time. Many of these pests have been managed through surveillance and various mitigation strategies, combined with follow-up analyses. Pest surveillance activities have been documented in the Canadian Prairies for over 100 years and analysis has progressed from determining the pest species involved, to understanding the damage they cause, their biology, spread, over-wintering strategies, reproduction, pesticide resistance, and genetic diversity. This research has generated a continuous history of the pest populations for crops in western Canada. Detailed virulence analysis has revealed pathogen evolution and adaptation to overcome some of the deployed host resistance genes. Some weed, insect, and plant pathogenic fungal species have evolved to become resistant to pesticides. Integration of pest surveillance activities will help to build a more responsive, robust, and reliable biovigilance program to manage crop pests in the Canadian Prairies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".