The success of GPS collar deployments on mammals in Australia
Bibliographic record
Abstract
Global Positioning System (GPS) wildlife telemetry collars are being used increasingly to understand the movement patterns of wild mammals. However, there are few published studies on which to gauge their general utility and success. This paper highlights issues faced by some of the first researchers to use GPS technology for terrestrial mammal tracking in Australia. Our collated data cover 24 studies where GPS collars were used in 280 deployments on 13 species, including dingoes or other wild dogs (Canis lupus dingo and hybrids), cats (Felis catus), foxes (Vulpes vulpes), kangaroos (Macropus giganteus), koalas (Phascolarctos cinereus), livestock guardian dogs (C. l. familiaris), pademelons (Thylogale billardierii), possums (Trichosurus cunninghami), quolls (Dasyurus geoffroii and D. maculatus), wallabies (Macropus rufogriseus and Petrogale lateralis), and wombats (Vombatus ursinus). Common problems encountered were associated with collar design, the GPS, VHF and timed-release components, and unforseen costs in retrieving and refurbishing collars. We discuss the implications of collar failures for research programs and animal welfare, and suggest how these could be avoided or improved. Our intention is to provide constructive advice so that researchers and manufacturers can make informed decisions about using this technology, and maximise the many benefits of GPS while reducing the risks.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".