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Record W2013158677 · doi:10.1139/y00-063

Acquisition and development of monkey tool-use: behavioral and kinematic analyses

2000· article· en· W2013158677 on OpenAlexvenueno aff
Hidetoshi Ishibashi, Sayaka Hihara, Atsushi Iriki

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2000
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMacaqueFlexibility (engineering)RakeKinematicsComputer scienceKey (lock)PsychologyArtificial intelligenceCommunicationBiologyNeuroscienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Four Japanese macaques were trained in the use of a T-shaped rake. Use the tool and development of the level of the skill of tool-use took place in three distinct stages. During stage 1, two of the monkeys seemed to use insight for initial solution, while fortuitous experiences led the other two monkeys to the solution. All the monkeys used the tool in a stereotyped manner and could retrieve food only when the tool was placed close to the food. At stage 2 the monkeys became able to manipulate the tool in various ways and became able to retrieve the food regardless of its position. By stage 3 they had developed the level of skill required for efficient retrieval. Further experiments revealed that the monkeys attempted to use unfamiliar objects which were similar to the original tool in shape, but not spherical or ring-shaped objects, to rake in the food.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.072
GPT teacher head0.335
Teacher spread0.263 · 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

Citations95
Published2000
Admission routes1
Has abstractyes

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