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Record W4224435162 · doi:10.1111/rec.13713

Creating entrances to tree cavities attracts hollow‐dependent fauna: proof of concept

2022· article· en· W4224435162 on OpenAlexaff
Murray Ellis, Jennifer E. Taylor, Susan G. Rhind

Bibliographic record

VenueRestoration Ecology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
FundersNSW Office of Environment and Heritage
KeywordsFaunaThreatened speciesSnagHabitatWoodlandVegetation (pathology)InvertebrateEcologyTree (set theory)GeologyBiology

Abstract

fetched live from OpenAlex

Redressing the paucity of tree hollows is essential for conservation of hollow‐dependent fauna in many landscapes around the world. We describe a method of accelerating availability of natural hollows in regenerating landscapes by mechanically creating entrances in tree stems that have existing voids or internal decay but have not yet developed entrances. We trialed this method in woodland and forest of south‐eastern Australia in 39 stems in the closely related tree genera Eucalyptus and Angophora . Exploratory drilling of 10‐mm diameter holes was used to detect the presence of internal decay or voids. We then drilled 40‐, 65‐, or 90‐mm diameter entrance holes, depending on the size of the potential cavity, 2.4–4.8 m above ground level. Camera traps showed that drilled entrances were investigated or used within hours of creation. A diverse suite of invertebrates, reptiles, mammals, and birds were recorded entering or leaving entrances, including threatened species. All 39 holes were used by animals with up to six vertebrate taxa using some entrances. Two bird species excavated material from within cavities, and three species of marsupial were recorded taking nesting material into the cavities. This trial provides evidence that adding entrances to currently inaccessible internal cavities in trees has potential to accelerate development of habitat for hollow‐dependent fauna, particularly in regenerating vegetation.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 teacher head, not a consensus.

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

Citations11
Published2022
Admission routes1
Has abstractyes

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