Evaluation of the technical performance of the Nofence virtual fencing system in Alberta, Canada
Bibliographic record
Abstract
• Network connectivity was optimal and virtual fence collar failures minimal while operating. • Solar charging varied between seasons, with no impact on battery charge or performance. • A limited number of collars were physically lost by cattle while grazing. • Technical performance was considered adequate in this northern temperate region. Virtual fence (VF) technology uses GPS-enabled collars to manage cattle movement through audio cues and electrical pulses, offering a potential alternative to traditional physical fencing. The performance of Nofence VF collars was evaluated while in operational mode and deployed on cattle grazing within the northern temperate climate of central Alberta, Canada. Technical parameters such as network connectivity, collar failures, battery performance, and solar charging capabilities of the VF collars were evaluated across four grazing trials, three conducted in summer and one in winter. The network connection intervals, defined as the time between successive connection events, ranged from 8.1 (± 6.2) to 9.4 (± 5.4) minutes throughout the trials, remaining well within the optimal 15-minute interval, highlighting the favourable interactivity with end-users. Poor network connections occurred less than 1 % of the time, demonstrating robust coverage across the entire area. Fourteen collars experienced a network connection failure that did not persist after a manual reset. Four cattle physically lost their collars, which were then recovered and promptly redeployed. Although the mean solar charging rate was lower during the winter trial (3.1 ± 10.8 mA h -1 ) than the summer trials (7.9 ± 18.0 to 12.4 ± 22.1 mA h -1 ), mean battery charge remained greater than 96 % for all trials, even during winter when daylight was limited. While reliable cellular network access is crucial, these results indicate that Nofence VF collars can effectively function in diverse environmental conditions, and may be suitable for broader adoption by cattle producers grazing in relatively cold climates, including those of western Canada.
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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 | 0.001 |
| 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".