A large-scale automated radio telemetry network for monitoring movements of terrestrial wildlife in Australia
Bibliographic record
Abstract
ABSTRACT Technologies for remotely observing animal movements have advanced rapidly in the past decade. In recent years, Australia has invested in an Integrated Marine Ocean Tracking (IMOS) system, a land ecosystem observatory (TERN), and an Australian Acoustic Observatory (A2O), but has not established movement tracking systems for individual terrestrial animals across land and along coastlines. Here, we make the case that the Motus Wildlife Tracking System, an open-source, rapidly expanding cooperative automated radio-tracking global network (Motus, https://motus.org) provides an unprecedented opportunity to build an affordable and proven infrastructure that will boost wildlife biology research and connect Australian researchers domestically and with international wildlife research. We briefly describe the system conceptually and technologically, then present the unique strengths of Motus, how Motus can complement and expand existing and emerging animal tracking systems, and how the Motus framework provides a much-needed central repository and impetus for archiving and sharing animal telemetry data. We propose ways to overcome the unique challenges posed by Australia’s ecological attributes and the size of its scientific community. Open source, inherently cooperative and flexible, Motus provides a unique opportunity to leverage individual research effort into a larger collaborative achievement, thereby expanding the scale and scope of individual projects, while maximising the outcomes of scant research and conservation funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".