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Record W2168981465 · doi:10.1071/wr06161

Predicting the occurrence of the quokka, Setonix brachyurus (Macropodidae : Marsupialia), in Western Australia’s northern jarrah forest

2007· article· en· W2168981465 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWildlife Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsSwampBiologyEcologyAkaike information criterionGeographyForestryStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.347
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it