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Record W2619772002 · doi:10.1093/jmammal/gyw157

Long-term simian research sites: significance for theory and conservation

2017· article· en· W2619772002 on OpenAlexafffund
Colin A. Chapman, Amélie Corriveau, Valérie A. M. Schoof, Dennis Twinomugisha, Kim Valenta

Bibliographic record

VenueJournal of Mammalogy · 2017
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsYork UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsWorld Wildlife FundNational Institutes of HealthNational Science Foundation
KeywordsEcologyTerm (time)SimianPopulationPrimateGeographyBiologySociologyDemography

Abstract

fetched live from OpenAlex

Simian primates (monkeys and apes) are typically long-lived animals with slow life histories. They also have varying social organization and can slowly impact their environment by either being seed dispersers or by overbrowsing their food trees. As a result, short-term studies and those focusing on just 1 location only provide a snapshot of simian life under a specific set of ecological conditions that typically do not represent the complete spatial and temporal picture. Long-term field studies are needed to obtain a true understanding of their behavior, life history, ecology, and the selective pressures acting on them. Fortunately, there have been many long-term studies of simians, so a great deal is known about many species. Here, we consider examples of long-term studies that have operated continuously for approximately a decade or more. We review studies that deal with ecophysiology, social organization, population and community ecology, or conservation. The information emerging from these sites is particularly helpful in the construction of informed conservation plans, which are desperately needed given the severity of threats to simians and the fact that responses do not occur over the duration of a Ph.D. or granting cycle (typically 1–3 years).

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)0.003
metaresearch head score (Gemma)0.001
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 score0.090
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.169
GPT teacher head0.463
Teacher spread0.294 · 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

Citations34
Published2017
Admission routes2
Has abstractyes

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