A Comparative Study of Onion Maggot “Delia Antiqua” Monitoring Techniques
Bibliographic record
Abstract
Onion maggots have reduced green onion and leek production in southern New Jersey for at least the last 80 years. Growers routinely apply soil insecticides at planting and spray for larvae and adult flies during the season. Two monitoring methods are available for determining adult fly activity. New York researchers have demonstrated that cone traps can be used. Two traps are placed near onion fields and checked for adult flies twice per week to determine peak fly emergence. Ontario researchers use yellow sticky cards for monitoring onion maggot flies in the onion fields. Three 10 × 15-cm cards are placed on each side of the field and are checked twice per week. An experiment was conducted in New Jersey to determine which system is more reliable and easier for consultants and growers to use. Two cone traps were placed at the edge of one onion field and yellow cards were placed in another field on four farms. The traps were checked twice per week from 29 Mar. to 22 Oct. Both monitoring methods tracked the adult flights, but the average number of flies captured was higher on the sticky cards. Ease of use is important if either system will be used as a monitoring tool. Sticky cards are more difficult to maintain since they must be replaced at least every 2 weeks. Since fields are irrigated or cultivated every week in southern New Jersey, the cards become covered with soil, reducing effectiveness. Also, it is more difficult to determine male and female flies on sticky cards.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".