Use of unmanned aerial vehicles (UAVs) and photogrammetric image analysis to quantify spatial proximity in beef cattle
Bibliographic record
Abstract
Spatial proximity is an important metric in cattle behaviour, which is used to study social structure, dyadic relationships, as well as grazing and maternal behaviours. We developed an efficient, novel, non-invasive method to quantify the spatial proximity of beef cattle by using UAV-based image acquisition and photogrammetric analysis. Orthomosaics constructed by images obtained from UAVs were used to measure, with an accuracy of ±1.96 m (95% likelihood), the inter-individual distances between cows and calves. Aerial videos of the calves and their dams, held in a 5 ha pasture, were made over four days using UAVs. We used two UAVs to video-capture the following: (i) the location of all individuals (UAV flown at 100 m) and (ii) the identity of cow–calf pairs (UAV flown at 15–30 m). Still-images extracted from the UAV-acquired video screenshots were used to produce orthomosaics. The orthomosaics captured all the cows and calves in a single image, from which we measured the distance between related and non-related cow–calf pairs. This UAV-based orthomosaic method clearly showed that members of related pairs were closer than non-related ones, and that the distance was greater in the evening, demonstrating the utility of UAVs to accurately measure cattle spatial proximity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".