Microwave sensors for detection of wild animals during pasture mowing
Bibliographic record
Abstract
Abstract. More than 400000 wild animals are killed or severely injured every year during spring time pasture mowing. Conventional methods for detection and removal or expulsion of animals before mowing are either inefficient or very time-consuming. The first really working method is based on a pyro-detector which senses the temperature contrast between the animals body and the surrounding pasture. Unfortunately, the detection reliability of this sensor decreases with increasing ambient temperature and strong sunlight, i.e. for typical weather conditions, when pasture is mowed, especially around noon. In this paper, a detector is presented that exhibits complementary behaviour. It works best during dry conditions (i.e. around noon), but has a tendency to false alarms when dew is present (i.e. morning and evening). The sensor is based on a commercial, low-cost Doppler module at 24GHz. It senses the difference of radar cross section between the animals body (high water content, specular reflection) and the pasture (low water content, diffuse reflection). The signal is analysed by means of a non-linear Wigner time-frequency transformation. Experimental results are presented for a laboratory setup as well as for measurement in actual spring-time pasture. The results prove that a microwave sensor is capable of reliably detecting animals of the size of a fawn even if it is covered by a layer of pasture.
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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 | 0.000 | 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) | 0.001 | 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".