Effectiveness of optical, digital, and hybrid zoom equipped drones for use in reading livestock ear tags for individual animal identification
Bibliographic record
Abstract
Predicting how advertised zoom capabilities of commercially available drones being deployed for animal management will perform can be difficult, as promotional and marketing materials supplied by the manufacturer do not necessarily reflect real-world performance. We compared our ability to read livestock ear tags used for individual animal identification using various drone models with differing zoom capabilities. Drone models were assessed at various distances using a veterinary bovine head model to determine their ability to read livestock ear tags of various colours and sizes, and to establish observational distance limits. Results indicate that while drones that primarily utilize optical zoom are preferable, newer model drones equipped with hybrid zoom cameras that utilize computational photography are superior to 5-year-old drone models equipped with only digital zoom cameras. Recently released drone models are now capable of reading livestock ear tags at distances exceeding 60 m and perform equivalent to binoculars in terms of discerning numbers printed on various coloured livestock ear tags.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".