Injury by <i>Contarinia</i> (Diptera: Cecidomyiidae) to early-seeded and late-seeded canola in northeastern Saskatchewan, Canada, and assessment of seed treatments for midge control
Bibliographic record
Abstract
Abstract A three-year field study in northeastern Saskatchewan, Canada, determined the effects of seeding date and seed treatment on feeding injury by Contarinia Róndani (Diptera: Cecidomyiidae) midges to canola, Brassica napus Linnaeus (Brassicaceae), and subsequent seed yield. Emergence cage monitoring indicated the presence of two generations of midges at the four locations observed. Intensity of midge injury to canola was low, but damaged plants were common. Field location and seeding date influenced probability of midge injury and canola growth stage. The wettest of the four locations had the greatest number of midges but low levels of probability of damage in two of three years. Early-seeded plots had higher injury probability than late-seeded plots, with the highest probability of injury occurring one to two weeks after flowering commenced. Seed treatment marginally affected midge injury ratings, suggesting that insecticide efficacy dissipated before the midges attacked. Seed yield was higher in early-seeded than in late-seeded plots in two of three years, despite the greater level of damage seen in early-seeded plots. Overall, the research showed that the agronomic benefits of early seeding outweighed the effects of the midge damage observed; therefore, producers in the Canadian prairie provinces should seed canola when conditions are most agronomically suitable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".