Factors affecting the detection of possums by spotlighting in Western Australia
Bibliographic record
Abstract
This paper describes how environmental factors, survey method procedures and differences in forest structure resulting from logging relate to the detection of koomal (common brushtail possum, Trichosurus vulpecula hypoleucus) and ngwayir (western ringtail possum, Pseudocheirus occidentalis). A total of 169 vehicle-based spotlight surveys of possums within native jarrah (Eucalyptus marginata) forest was conducted on three transects over eight years (1996–2003). Up to 5.7 koomal and up to 3.3 ngwayir were detected per kilometre per transect side. Only one ngwayir was detected during the eight surveys conducted between 2001 and 2003. More koomal were seen in spring and autumn (i.e. September–November and March–May respectively) and more ngwayir were seen between October and April. Although surveys were not conducted on very rainy or excessively windy nights, fewer possums were nonetheless seen on nights following rainy days and on cold nights. Cloud cover also affected sightings of koomal. The time taken to complete the surveys increased in conjunction with the number of possums detected, on account of the time required to record data. The importance of standardising travelling speed also is emphasised. Possum spotlight counts differed between recently logged and unlogged areas. However, these findings were not supported by complementary koomal abundance estimates derived from trapping, suggesting that vegetation structure may affect detectability. Factors such as the lunar cycle, wind speed and survey start time after sunset did not significantly affect detection rates of either species. On the basis of these findings, specific survey conditions can be selected to improve spotlight detection efficiency.
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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 | 0.002 |
| 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 | 0.001 |
| 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".