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Record W2089657169 · doi:10.1071/zo11047

Ecology of Stimson’s python (Antaresia stimsoni) in the MacDonnell Ranges of central Australia

2011· article· en· W2089657169 on OpenAlexaff
Peter J. McDonald, Gary Luck, Skye Wassens, Chris R. Pavey

Bibliographic record

VenueAustralian Journal of Zoology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsDepartment of Environment and Conservation
FundersCharles Sturt UniversityUniverzita Karlova v Praze
KeywordsTransectEcologyHome rangeHabitatPython (programming language)BiologyRiparian zoneFoothillsPredationRange (aeronautics)Geography

Abstract

fetched live from OpenAlex

Stimson’s python (Antaresia stimsoni) is a small nocturnal python (Pythonidae) that occurs throughout Australia’s arid zone. Despite its wide distribution and localised abundance, no field-based studies have been undertaken on this species. We investigated activity patterns, habitat use, diet, and body sizes of A. stimsoni in the MacDonnell Ranges bioregion of the Northern Territory. Data were collected at night by road-cruising along a sealed road transect over 12 consecutive months. We found that the species copes with the extreme weather variability of arid Australia by remaining active over a broad range of air temperatures and maximising activity following rainfall when relative humidity is high and ground-dwelling frogs, a significant prey source, are likely to be abundant. A. stimsoni ceased activity only during the coldest months of the year. The species occurs in a range of vegetation types, with an apparent preference for riparian woodland, and its ability to thrive in the MacDonnell Ranges may be related to the abundance of rock-outcrops for refuge. Our results for diet and body size support previous research based on museum specimens, confirming that A. stimsoni feeds on a range of terrestrial vertebrates and is not sexually dimorphic.

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 score0.008
Threshold uncertainty score0.994

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.044
GPT teacher head0.259
Teacher spread0.215 · 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
Published2011
Admission routes1
Has abstractyes

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