Localizing Japanese toads in a mountainous terrain using drone-based radiotelemetry
Bibliographic record
Abstract
Monitoring the movement of small animals is a fundamental aspect of ecological studies as well as spatially explicit conservation and management. However, this remains a challenging task especially in mountainous terrains. Although drone-based radiotelemetry (DRT) is employed to localize animals, its application in mountainous terrains is limited by the collision risks associated with undulating terrains as well as the obstruction of signals by dense vegetation and steep slopes. We addressed these challenges by generating fine-scale three-dimensional maps and moving vertically mounted directional antennas in a double grid pattern, scanning both in longitudinal and latitudinal grids. This new DRT system was helpful in localizing four adult Japanese toads ( Bufo japonicus) living in hiding places typical of mountainous terrains. All toads were located within 1–60 days of being released. Transmitter signals were detected within two consecutive flights (three flights in one case). Instances of transmitter detection were significantly biased when the drone was facing either direction of the double-grid path, indicating that the double-grid pattern had reduced detection failure. The absolute localization error ( n = 48) of 22.4 ± 21.0 m (44.8 ± 42% of the transmitter-to-receiver distance) was lower than that reported in a previous study conducted in a similar mountainous terrain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".