Evaluating the establishment potential of cabbage stem flea beetle (Coleoptera: Chrysomelidae) and pollen beetle (Coleoptera: Nitidulidae) in canola-growing regions of North America using ensemble species distribution models
Bibliographic record
Abstract
Cabbage stem flea beetle, Psylliodes chrysocephala (Linnaeus 1758), and pollen beetle, Brassicogethes viridescens (Fabricius 1787), are pests of oilseed rape [Brassica spp. (Brassicales: Brassicaceae)] crops in Europe and pose a potential threat to canola production in North America. We used species occurrence and environmental data to develop ensemble species distribution models describing P. chrysocephala and B. viridescens habitat suitability, creating risk maps for either species under current (1981-2010; globally) and future [2011-2040 and 2041-2070, across 2 IPCC Shared Socio-economic Pathways (SSPs); North America only] environmental conditions. Projections for both species show improvement in northern North American habitat suitability under either SSP over time. Crop dominance was the most important predictor of suitable habitat for both species, followed by mean annual temperature range, precipitation metrics, and elevation (P. chrysocephala only). Risk maps for P. chrysocephala show broad habitat suitability, increasing under future scenarios, for this insect if it becomes introduced to North America; however, a phenological mismatch between P. chrysocephala, which specializes on winter oilseed rape (WOSR) in Europe, and spring oilseed rape (SOSR) would likely inhibit the long-term persistence of this insect in central North America. For B. viridescens, which impacts SOSR in Europe and is present in northeastern North America, predictive maps show increased risk in discontinuous patches across central North America that improve in suitability over time. While SOSR-cropping systems in central North America are environmentally suitable for both P. chrysocephala and B. viridescens, the establishment potential of these species may depend upon future sowing practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".