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Record W1971893746 · doi:10.1159/000082450

The Short-Term Effects of a Hurricane on the Diet and Activity of Black Howlers (Alouatta pigra) in Monkey River, Belize

2005· article· en· W1971893746 on OpenAlexafffund
Alison M. Behie, Mary S. M. Pavelka

Bibliographic record

VenueFolia Primatologica · 2005
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Geographic Society
KeywordsDry seasonPopulationBiologyHabitatWet seasonEcologyHome rangeAnimal scienceDemography

Abstract

fetched live from OpenAlex

The diet and activity of a population of Alouatta pigra were compared before and immediately after a major hurricane to begin to explore how the monkeys cope with severe habitat destruction. Focal animal data were collected from January to April (dry season) for two seasons before (368 h) and one season after the storm (149 h) on a population of black howlers in Monkey River, Belize. During the first dry season after the storm, the monkeys changed their diet in direct accordance with the availability of food. The absence of fruit and flower production and the increase in new leaf availability forced the monkeys to adopt a completely folivorous diet. The activity budget of the monkeys also changed, and they spent more time inactive, which may be linked to the change in the distribution and type of food available. They also spent less time in social interactions, which may be due to the lower number of juveniles in the population or to the formation of new groups between unfamiliar individuals following the hurricane. The ability to live for long periods of time on leaves alone has allowed the remaining population to survive in the short term.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.025
GPT teacher head0.308
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2005
Admission routes2
Has abstractyes

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