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Record W4385609447 · doi:10.1163/1568539x-bja10230

Tool-assisted water scooping in Balinese long-tailed macaques

2023· article· en· W4385609447 on OpenAlexafffund
Camilla Cenni, Bernard Thierry, I Nengah Wandia, Jean‐Baptiste Leca

Bibliographic record

VenueBehaviour · 2023
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Lethbridge
KeywordsContext (archaeology)PopulationObject (grammar)PsychologyGeographyArtificial intelligenceComputer scienceSociologyDemographyArchaeology

Abstract

fetched live from OpenAlex

Abstract While tool use has been widely reported in non-human animals for food acquisition, the use of tools for drinking has been largely overlooked, with primates being a notable exception. We documented tool-assisted water scooping and drinking in several Balinese long-tailed macaques ( Macaca fascicularis ), living in Ubud, Indonesia, over a period of four years. We observed repeated tool-assisted water scooping using leaves, nuts, pits, and stones. Our results indicate that this behaviour is associated with manual drinking and can be performed in a playful context. This population habitually engages in a cultural form of stone-assisted object play known as stone handling, and it has an overall propensity to manipulate objects in water. We discuss the relationship between instrumental and non-instrumental object-assisted actions, as well as the possibility for this behaviour to be a tradition in this population. This report offers new insights into the limited literature on tool-assisted drinking in monkeys.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
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 head0.058
GPT teacher head0.367
Teacher spread0.310 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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