A comparison of survey methods for arboreal possums in jarrah forest, Western Australia
Bibliographic record
Abstract
Comparative trials of different survey methods were conducted in the southern jarrah (Eucalyptus marginata) forest to determine the most efficient means of detecting koomal (common brushtail possum, Trichosurus vulpecula hypoleucus) and ngwayir (western ringtail possum, Pseudocheirus occidentalis). In particular, we examined different trapping and spotlighting methods and compared these with scat surveys. Six different trapping methods (derived by combining three bait types and two trap positions) were compared at six sites. Significantly fewer koomal were caught on ‘universal’ bait (i.e. peanut butter, rolled oats and sardines) than on flour-based baits using rose oil or Eucalyptus oil as lures. Significantly more individuals of both possum species were caught in arboreal traps than in ground traps (P < 0.001 in both cases). Recapture rates of koomal were high, whereas ngwayir were rarely retrapped. There were no detection differences between six different spotlighting methods (derived by combining three spotlight intensities with two filter colours) for koomal. Significantly more ngwayir were detected using 50-W or 100-W lights than 20-W lights (P = 0.01). There were no significant differences in the detection rates for ngwayir using red or white light. There were, however, significant observer differences in the number of possums of both species detected (koomal, P = 0.025; ngwayir, P = 0.004). Spotlighting detected, on average, only 4.9% of the koomal ‘known to be alive’ by trapping. However, spotlighting with a 50-W or 100-W spotlight detected more ngwayir than did trapping. Koomal abundance measures derived from scat surveys were not related to trapping or spotlight abundance estimates. For ngwayir, however, scat counts were strongly related to spotlight counts and there were no significant observer differences for the former. We conclude that koomal are more effectively surveyed using arboreal trapping with rose or Eucalyptus lures. Ngwayir are best surveyed using scat surveys or 50-W spotlights.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".