Contrasting late season pest insect abundance in non‐crop vegetation areas and nearby canola fields in the Canadian Prairies
Bibliographic record
Abstract
Abstract Non‐crop vegetation areas in agricultural landscapes are vital for maintaining biodiversity. However, they potentially host pest insects, which can cause economic loss in crop fields. Some insect species have been found to spill into crops from these areas, but this varies depending on species, landscape composition and the time of the season. To determine if five common pest insects of canola crops were spilling into fields during the late growing season, we collected samples at various distances from non‐crop areas, in a part of the Canadian Prairies (Alberta, Canada) where this crop is widely grown. Sampling occurred at 15 sites in each of 10 fields ( N = 150 sites). We modelled changes in pest abundance over distance from the non‐crop areas and contrasted the abundance of each taxon in the crop and non‐crop areas. Only leafhoppers (Hemiptera: Cicadellidae) demonstrated a declining gradient in abundance that is consistent with spillover from non‐crop vegetation areas into the canola fields. Weevils were found to have significantly higher abundance in the non‐crop areas, indicating a relationship between this taxon and the non‐crop area in the late season, but there was no decline in abundance, which might indicate spillover occurring. All taxa demonstrated spatial differences in abundance among fields. This study found limited evidence that the pests are spilling over from non‐crop vegetation into canola crops during the late season. Therefore, movement of pests from non‐crop vegetation areas at this time is unlikely to be a driver of pest pressure for this economically important crop.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".