Harmonic radar: efficacy at detecting and recovering insects on agricultural host plants
Bibliographic record
Abstract
BACKGROUND: In pest management research, harmonic radar systems have been largely used to study insect movement across open or vegetation-poor areas because the microwave signal is attenuated by the high water content of vegetation. This study evaluated whether the efficacy of this technology is sufficient to track insects in vegetative landscapes. RESULTS: Field efficacy data were collected using portable harmonic microwave radar and electronic dipole tags mounted on adults of three economically important pests: Leptinotarsa decemlineata (Say), Diabrotica virgifera virgifera (LeConte) [corrected] and Conotrachelus nenuphar Herbst. Detection and recovery of tagged Colorado potato beetles, plum curculios and western corn rootworms was high within and among potato plants, moderate within apple trees and high within, but not between, corn plants respectively. The efficacy of the radar depends on the ability of the operator to move around the host, scanning for a signal 'sightline' with the tagged insect among plant structures. CONCLUSION: The detection rate of tagged insects by harmonic radar systems is high enough to track the walking path of pests through low row crops such as potato, tall row crops such as corn or tall but well-separated trees of orchard-type crops by adapting the scanning procedure to the vegetative architecture.
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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) | 0.000 | 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) | 0.002 | 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".