Virtual fencing in remote boreal forests: performance of commercially available GPS collars for free-ranging cattle
Bibliographic record
Abstract
Abstract Background The use of virtual fencing in cattle farming is beneficial due to its flexibility, not fragmenting the landscape or restricting access like physical fences. Using GPS (Global Positioning System) technology, virtual fence units emit an audible signal and a low-energy electric shock when crossing a predefined border. In large remote grazing areas and complex terrains, where the performance of the GPS units can be affected by landscape structure, increased positioning errors can lead to unnecessary shocks to the animals leading to animal welfare concerns. This study aimed to explore factors affecting the GPS performance of commercially available virtual fence collars for cattle (NoFence©), both using static tests and mobile tests, i.e., when deployed on free-ranging cattle. Results The static tests revealed generally high fix success rates (% successful positioning attempts), and a lower success rate at four of 30 test locations was most likely due to a lack in GSM (Global System for Mobile communications) coverage. On average the GPS precision and accuracy errors were 3.3 m ± 2.5 SD and 4.6 m ± 3.2 SD, respectively. We found strong evidence that the GPS precision and accuracy errors increased errors under closed canopies. We also found evidence for an effect of the sky-view on the GPS performance, although at a lesser extent than canopy. The direction of the accuracy error in the Cartesian plane was not uniform, but biased, depending on the aspect of the test locations. With an average of 10.8 m ± 6.8 SD, the accuracy error of the mobile tests was more than double that of the static tests. Furthermore, we found evidence that more rugged landscapes resulted in higher GPS accuracy errors. However, the error from mobile tests was not affected by canopy cover, sky-view, or cattle behaviors. Conclusions This study showed that GPS performance can be negatively affected by landscape complexity, such as increased ruggedness and covered habitats, resulting in reduced virtual fence effectiveness and potential welfare concerns for cattle. These issues can be mitigated through proper pasture planning, such as avoiding rugged areas for the virtual fence border.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".