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Record W2114207382 · doi:10.1071/zo08076

Factors influencing population dynamics in island and mainland populations of the swamp antechinus (Antechinus minimus)

2008· article· en· W2114207382 on OpenAlexaff
M. G. Sale, Barbara A. Wilson, John P. Y. Arnould

Bibliographic record

VenueAustralian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsDepartment of Environment and Conservation
FundersParks Victoria
KeywordsBiologyEcologySwampPopulationMainlandHabitat

Abstract

fetched live from OpenAlex

Simultaneously analysing demographic processes of small mammals living in different ecological contexts may help to understand mechanisms that influence the growth and decline of these populations. The size and demography of swamp antechinus (Antechinus minimus) populations located in a coastal mainland habitat and on a small offshore island in south-eastern Australia were investigated. Large demographic differences occurred between the two ecosystems, with the island population density often 100 times greater than that on the mainland. The swamp antechinus in the mainland habitat was influenced by extrinsic climatic forces, with juvenile recruitment, individual body mass and overall population size being affected by rainfall, a factor likely to influence food availability for the species. However, the island population did not appear to be affected by drought to the same degree where allochthonous marine nutrient inputs may have offset any drought-induced reduction in primary production. Significantly greater juvenile recruitment in the island habitats combined with restricted emigration and potentially reduced predation and interspecific competition are likely to be responsible for the high population densities on the island. Although island populations appear robust, future conservation efforts should focus on mainland populations given the genetic deficiencies in the island populations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.501

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)0.0000.000

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 head0.027
GPT teacher head0.244
Teacher spread0.217 · 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.

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

Citations19
Published2008
Admission routes1
Has abstractyes

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