MétaCan
Menu
Back to cohort
Record W1996414013 · doi:10.1071/wr05021

Estimating the abundance of eastern grey kangaroos (Macropus giganteus) in south-eastern New South Wales, Australia

2006· article· en· W1996414013 on OpenAlexaff
Anthony Pople, S. C. Cairns, Norbert Menke, Nicholas H. Payne

Bibliographic record

VenueWildlife Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsGeographyTransectAerial surveyAbundance (ecology)PopulationWildlife managementPopulation densityWildlifeForestryFisheryEcologyAgroforestryEnvironmental scienceBiologyCartography

Abstract

fetched live from OpenAlex

To provide an estimate of kangaroo numbers for harvest management, a survey was designed for an area of 29 500 km2 encompassing the agricultural and grazing lands of the Braidwood, Cooma, Goulburn, Gundagai and Yass Rural Lands Protection Board (RLPB) districts in south-east New South Wales. An aerial survey using a helicopter was considered more efficient than ground survey because of the size of the area, relatively high relief and dense tree cover, and the need for regular monitoring. Tree cover and landscape relief was used to stratify the five RLPB districts into areas of probable high, medium and low kangaroo density. Kangaroo density estimated from helicopter surveys conducted in the Northern Tablelands of New South Wales was used to suggest densities and thereby allocate survey effort in each stratum. A survey comprising 735 km of transect line was conducted in winter 2003 with a target precision of 20%. The survey returned an estimate of 286 600 ± 32 300 eastern grey kangaroos for the whole of the proposed south-east New South Wales kangaroo-management zone. In 2004, a trial harvest of slightly less than 15% of this estimate was taken. Success of the trial will be determined by the impact of harvesting on the population’s dynamics, by landholder and industry participation, and by the capacity to monitor population size, harvest offtake and compliance with regulations.

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 score0.012
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.067
GPT teacher head0.325
Teacher spread0.257 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueWildlife ResearchSame topicWildlife Ecology and ConservationFrench-language works237,207