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Habitat selection, diet and interspecific associations of the rufous‐tailed weaver and Fischer’s lovebird

2008· article· en· W1971117239 on OpenAlexaff
Ephraim Mwangomo, L.H. Hardesty, A. R. E. Sinclair, Simon Mduma, Kristine L. Metzger

Bibliographic record

VenueAfrican Journal of Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of British Columbia
FundersUnited States Agency for International Development
KeywordsStarlingBiologyHabitatFlockInterspecific competitionZoologyEcology

Abstract

fetched live from OpenAlex

Abstract We investigated habitat selection and diets of two co‐occurring endemic bird species (rufous‐tailed weaver, Histurgops ruficauda , Fischer’s lovebird, Agapornis fischeri ) and four other species with which they associated in mixed feeding flocks (red‐billed buffalo weaver, Bubalornis niger , white‐headed buffalo weaver, Dinemellia dinemellii , superb starling, Lamprotornis superbus , and Hildebrandt’s starling, Lamprotornis hildebrandti ) during the dry season in Serengeti National Park Tanzania. Mixed species flocks could facilitate food acquisition and/or act as anti‐predator mechanisms. Five of the six species selected grassland habitat over Acacia habitat along transects. Analysis of species association, using Cole’s coefficient of association, showed that both rufous‐tailed weavers and superb starlings co‐occurred with red‐billed buffalo weavers. Superb starlings were negatively associated with Fischer’s lovebird, Hildebrandt’s starling and white‐headed buffalo weavers. Diet analysis revealed that the rufous‐tailed weaver, white‐headed buffalo weaver, and red‐billed buffalo weaver were generalists eating both insects and seeds, whereas Fischer’s lovebird and superb starling were specialists, selecting only seeds and insects respectively. These data offer some support for the hypothesis that mixed species flocks facilitate mutual food searching.

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 score0.004
Threshold uncertainty score0.341

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 studies0.0000.001
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 head0.020
GPT teacher head0.228
Teacher spread0.207 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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