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Record W2078217625 · doi:10.1071/zo06064

Structures of bird communities in woodland remnants in central New South Wales, Australia

2007· article· en· W2078217625 on OpenAlexaff
Sue Briggs, Julian Seddon, Stuart Doyle

Bibliographic record

VenueAustralian Journal of Zoology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
FundersCommonwealth Scientific and Industrial Research Organisation
KeywordsWoodlandSpecies richnessCypressInsectivoreHabitatEcologyGeographyBiology

Abstract

fetched live from OpenAlex

The overall aim of this study was to investigate structures of bird communities in remnants of fragmented box/cypress pine woodlands in central New South Wales, Australia, to guide habitat rehabilitation. The aims of the study were to: (1) determine how bird densities and species richness varied with remnant category; (2) determine how ranked densities of bird species varied by feeding group with remnant category; and (3) provide information on structures of bird communities in box/cypress pine woodlands to guide restoration. Structures of bird communities varied with remnant category. Large remnants had the most species whereas medium-sized and small remnants in low condition had the fewest. Bird densities increased with decreasing remnant area although densities did not differ significantly between remnant categories. Ranked bird densities varied between remnant categories, with relatively even distributions in large remnants in high condition, and uneven distributions in small remnants in low condition. Densities of small insectivores were much lower in small, low-condition remnants than in large, high-condition remnants. Densities of generalists such as noisy miner and galah showed the reverse pattern. The structures of bird communities in large remnants in good condition provide a reference state for assessing recovery of bird communities.

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)0.001
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 score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.038
GPT teacher head0.274
Teacher spread0.236 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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