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Record W2052722082 · doi:10.1071/wr03098

The influence of predation risk on foraging behaviour of brushtail possums in Australian woodlands

2005· article· en· W2052722082 on OpenAlexaff
Karolyne N. Pickett, David S. Hik, A. E. Newsome, Roger P. Pech

Bibliographic record

VenueWildlife Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of AlbertaThe Scarborough HospitalUniversity of TorontoFisheries and Oceans Canada
Fundersnot available
KeywordsForagingPredationWoodlandHabitatEcologyBiologyWildlife conservationWildlifeFaunaAbundance (ecology)

Abstract

fetched live from OpenAlex

The potential effects of predation risk on common brushtail possums were investigated in south-eastern Australian woodlands. Patterns of habitat use, foraging costs using giving-up density (GUD) experiments, and indices of body condition and reproductive success were examined at eight sites in two habitat types (eucalypt- or cypress-pine–dominated stands), within three areas of different red fox abundance (high, moderate and low fox density). In cypress-pine–dominated stands, possums travelled further on the ground, visited more feeding stations and had lower GUDs at feeders where foxes were removed than did possums in high-fox-density sites. In contrast, there was no effect of fox removal on the behaviour of possums in eucalypt-dominated stands. Fox removal also had no effect on indices of body condition and reproduction. Minor effects of microhabitat were detected with trackplot and GUD experiments, but, overall, the results suggest that habitat at the stand-level was more important. The non-lethal effects of foxes in different habitats may need to be taken into account when developing conservation strategies for native marsupials.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.326
Teacher spread0.295 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2005
Admission routes1
Has abstractyes

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