An evaluation of transect, plot and aerial survey techniques to monitor the spatial pattern and status of the bilby (Macrotis lagotis) in the Tanami Desert
Bibliographic record
Abstract
We evaluated three monitoring techniques to determine the spatial pattern and relative abundance of the bilby (Macrotis lagotis) in the Tanami Desert, Northern Territory. All the methods examined relied on the identification of animal sign (foot imprints or diggings) to indicate the presence of a species. With fixed transects, a 10-km prepared tracking surface was monitored regularly using an all-terrain vehicle. With random plots, an unprepared tracking surface within a 200 × 300 m area was searched on foot for sign of the species. A helicopter was used in an aerial survey to identify bilby diggings from an altitude of 15–20 m while travelling at a speed of 30–40 knots along a predefined transect. The results for each method were stratified in relation to latitude and substrate to facilitate comparison of the efficacy of each technique. The fixed transects returned the least number of bilby records for most effort. The aerial transect technique resulted in few (<4%) false negative records but a sizeable (42%) number of false positive records. It is suggested that the aerial survey technique combined with ground-truth survey plots would provide reliable information on the extent of occurrence and status of the bilby in the remote spinifex deserts of central Australia.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".