MidgEmerge, a new predictive tool, indicates the presence of multiple emergence phenotypes of the overwintered generation of swede midge
Bibliographic record
Abstract
Abstract The swede midge, Contarinia nasturtii (Kieffer) (Diptera: Cecidomyiidae), is a pest of cruciferous crops (Brassicaceae) in Europe and North America with high potential for economic impact. Effective timing of insecticide applications for swede midge control is difficult, in part due to a short adult lifespan. Predictive models are often used in integrated pest management programmes to facilitate the timing of control strategies. A European model, Contapré, for predicting adult swede midge emergence was shown to be inaccurate under Ontario field conditions. A new predictive model, MidgEmerge, was developed using DYMEX™ modelling software. MidgEmerge accurately predicts swede midge emergence in both Ontario and Québec. Observed emergence patterns cannot be explained without the presence of multiple emergence phenotypes. MidgEmerge indicates that there are two emergence phenotypes of the swede midge, each completing four generations per year in southern Ontario. A fifth generation of each may become possible with climate change. Evidence of a possible third emergence phenotype is presented. MidgEmerge has the potential to be an important predictive tool to inform and direct integrated pest management practices targeted against swede midge in North America.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".