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Record W2087010991 · doi:10.7882/az.2014.036

Multiple species use of a water-filled tree hollow by vertebrates in dry woodland habitat of northern New South Wales

2014· article· en· W2087010991 on OpenAlexaff
Dana Vickers, John T. Hunter, Wendy Hawes

Bibliographic record

VenueAustralian Zoologist · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity College of the North
Fundersnot available
KeywordsWoodlandHabitatEcologyResource (disambiguation)MammalNocturnalBiologyForaging

Abstract

fetched live from OpenAlex

Tree hollows are a major feature within Australian habitats and an important functional resource for many species in terms of shelter, reproduction, and thermoregulation. Water-filled tree hollows, or phytotelmata, also function as a valuable resource, but their use is only scarcely documented. We used camera trapping to determine which vertebrate species were utilising a known water-holding hollow in dry woodland habitat, and assessed whether antagonistic behaviour, such as hoarding of the resource, was occurring. Camera footage was obtained over a period of three days and nights, and species’ use of the hollow analysed. A total of seven vertebrates (one frog, two reptile and four mammal species) were recorded using the hollow, which included diurnal and nocturnal species. Use by the Feathertail Glider was the most frequent compared to other species. The study highlights an ecological significance of water-filled hollows that should be considered in the management of dry woodland habitats, where the availability of these resources may be depleted by land clearing and loss of existing hollow-bearing trees.

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)0.000
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.026
GPT teacher head0.210
Teacher spread0.183 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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