Predicting the occurrence of the quokka, Setonix brachyurus (Macropodidae : Marsupialia), in Western Australia’s northern jarrah forest
Bibliographic record
Abstract
The quokka, Setonix brachyurus, is a medium-sized, macropodid marsupial that is endemic to south-western Australia. It has declined markedly in its distribution and abundance since the early 1930s and is listed as vulnerable under IUCN criteria. The presence or absence of quokka populations at 66 sites in the northern jarrah forest of Australia was investigated using generalised linear models (GLM). We hypothesised that fox control and the presence of a mosaic of post-fire seral stages within Agonis linearifolia swamp vegetation were important in predicting the presence of quokkas. The number of poison meat baits delivered per hectare, the average number of years since the swamps burnt and the number of post-fire age classes within the swamps (mosaic value) were used as explanatory variables. Two models had substantial support (?AICc < 2), with the best approximating model including the variables ‘baiting’ and ‘swamp age’, and the second-best model including the additional variable ‘swamp mosaic value’. The two best models had Akaike weights (weight of evidence as being the best model of the data) of 0.465 and 0.308 respectively. We used an information-theoretic approach and multimodel inference to determine the best approximating model of baiting, swamp age and swamp mosaic, and Akaike weights to assess model fit and to rank variable importance. Baiting had a model average parameter estimate of 98, swamp age 79 and a mosaic of swamp age classes 42, implying that baiting was more than twice as important as the number of swamp ages classes at a site in predicting the occurrence of quokkas. Evidence from our analysis therefore supports previous studies that concluded that continued fox control and the maintenance of a mosaic of early seral stage (<10 years since fire) and long unburnt habitat (>19 years since fire) are essential for its conservation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".