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Spatial distribution of feral house mice during a population eruption

2004· article· en· W2544498075 on OpenAlexvenueno aff
Jens Jacob, Hannu Ylönen, Grant R. Singleton

Bibliographic record

VenueEcoscience · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatPopulationEcologyHouse miceCropForagingAbundance (ecology)BiologyAgroecosystemGeographyAgriculture

Abstract

fetched live from OpenAlex

:Seasonal movements of rodents in agro-ecosystems from refuge habitats to impact habitats could reflect tracking of resources by mice and may be linked to population eruptions. We monitored the abundance of house mice in refuge habitat (fencelines) and impact habitats (crops) at four farms in southeastern Australia using capture-mark-release trappings during a population eruption. For most of the year, mice did not prefer fencelines to the adjacent “sea of grain crops”, but 3 to 4 months post-harvest more mice populated refuge habitats than impact habitats. This preference for refuge habitat coincided with a considerable increase in mouse abundance and the depletion of food in the stubble of the harvested crops. Therefore, habitat choice by mice seems to predominantly track resources such as food and shelter. The percentage of recaptures within months was highest in the crop habitat, indicating higher site fidelity for mice caught there. Mice captured in crop habitats were generally representative of the demographic structure of those mice living along the crop margin. Six months post-harvest, mice living in crops were smaller, and possibly lower-quality emigrants from the fencelines. Foraging movements measured with the fluorescent biomarker Rhodamine B commonly extended about 20-30 m from the fenceline into the crop.

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 score0.066
Threshold uncertainty score0.448

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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
Published2004
Admission routes1
Has abstractyes

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