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Record W2122789298 · doi:10.1644/11-mamm-a-411.1

White-lipped peccary home-range size in a protected area and farmland in the central Brazilian grasslands

2013· article· en· W2122789298 on OpenAlexaff
Anah Tereza de Almeida Jácomo, Mariana Malzoni Furtado, Cyntia Kayo Kashivakura, Jader Marinho‐Filho, Rahel Sollmann, Natália Mundim Tôrres, Leandro Silveira

Bibliographic record

VenueJournal of Mammalogy · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Calgary
FundersMemphis ZooWorld Wildlife Fund
KeywordsNational parkHome rangeGeographyRange (aeronautics)Dry seasonEcologyCropAgroforestryForestryPhysical geographyEnvironmental scienceBiologyHabitatArchaeologyCartography

Abstract

fetched live from OpenAlex

White-lipped peccaries (Tayassu pecari) are important seed predators and dispersers throughout the Neotropics. Because they occur in groups as large as 300 individuals, they need large areas to persist. We investigated homerange size and overlap of 13 groups using radiotelemetry data from 3 years of monitoring in the Emas National Park and surrounding farmland in central Brazil. Average home-range sizes were 1,710.64 ha for 50% of the locations and 8,659.99 ha for 95% based on minimum convex polygons and 790.38 ha for 50% of the locations and 7,986.92 ha for 95% based on the fixed kernel estimator. Home-range size did not correlate with group size, the monitoring period, or the number of locations obtained. Home ranges were larger during the wet season than the dry season. Average home-range overlap among groups was 31%; there were no significant differences in overlap between seasons. Home ranges varied seasonally, most likely in response to the dynamic landscape of crop plantations surrounding the park. Although the peccaries fared well in the heterogeneous agricultural landscape surrounding the park, conflict with farmers due to crop damage and landscape changes due to expansion of sugarcane plantations need to be addressed by conservation strategies.

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 score0.007
Threshold uncertainty score0.871

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)0.0010.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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