Making control decisions for <i>Sitodiplosis mosellana</i> (Diptera: Cecidomyiidae) in wheat (Gramineae) using sticky traps
Bibliographic record
Abstract
Wheat midge larvae, Sitodiplosis mosellana (Géhin), attack developing seeds and cause losses of common wheat, Triticum aestivum L., and durum wheat, Triticum durum Desf. (Olfert et al. 1985; Lamb et al. 1999). Insecticide applied as the crop flowers can reduce damage (Elliott 1988). The economic threshold varies from 4 to 7% of common wheat seeds infested by larvae, depending upon grade of wheat and cost of insecticidal control (Lamb et al. 2000). To make control decisions, farmers count adults at sunset during the period from heading to flowering (Elliott and Mann 1996), and apply insecticide if densities reach a nominal threshold of one adult per four or five wheat spikes (Anonymous 1993). Decisions must be made although no relationship has been detected between the number of adults in the crop and subsequent densities of damaging larvae (Oakley et al. 1998), and farmers have little confidence in their counts. The pest is difficult to sample because adults are small, short-lived, crepuscular, and are usually hidden in the crop canopy (Pivnick and Labbé 1993); eggs are microscopic (Mukerji et al. 1988); larvae feed inside florets (Lamb et al. 2000); and mature larvae and pupae are in the soil (Lamb et al. 1999). Sticky traps have been used to sample midges in sorghum (Merchant and Teetes 1992) and in wheat (Oakley et al. 1998), with mixed results. Our objective was to determine if catches of wheat midge adults on sticky traps can effectively guide farmers in making control decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".