Use of unmanned aerial system to assess wildlife (<i>Sus scrofa</i>) damage to crops (<i>Zea mays</i>)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Damage caused by ungulates to agricultural areas is difficult to evaluate because the real extent of the damage remains usually poorly described and potentially leads to conflicts. Recent advances in unmanned aerial systems (UAS) provide new versatile mapping and quantification possibilities in a wide range of applications. We used crop fields (Zea mays) damaged by wild boar (Sus scrofa) and compared the extent of the damage by means of three methods: (i) traditional ground-based assessment; (ii) UAS orthoimages with operator delineation; and (iii) UAS crop height model with automatic delineation based on height threshold. We showed for the first time that UAS can be applied for assessing damage of ungulates to agriculture. The two methods using UAS imagery provide coherent and satisfactory results and tend to underestimate the damage area when compared to in-use ground-based field expertise. However, we suggest that performance of UAS should further be tested in variable conditions to assess the broad application of this tool. Our study describes the potential of UAS as a tool for estimating more accurately the damage area and subsequently the compensation costs for wildlife damage. The proposed approach can be used in support of local and regional policies for the definitions of compensation for farmers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it