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Record W2781124803 · doi:10.5539/ijb.v10n1p1

Can Myrmica rubra Ants Use Tools or Learn to Use Them?

2017· article· en· W2781124803 on OpenAlexvenueno aff
Marie‐Claire Cammaerts

Bibliographic record

VenueInternational Journal of Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsThread (computing)BiologyComputer scienceProgramming language

Abstract

fetched live from OpenAlex

The aim of this study was to define the limit of ant cognition, and we examined whether Myrmica rubra ants could use tools or learn to use them. We presented the ants with 1) a piece of mealworm inserted into a small tube tied to a thread that had to be pulled for easy access to the mealworm; 2) a plug that closed the entrance of the ant sugar water tube provided with two push-pieces that had to be pushed to remove the plug from the entrance; and 3) a plug, closing the nest entrance, provided with a thread that had to be pulled to remove the plug. The ants could not use these “proto-tools”. After exposure to proto-tools having been used, some ants interacted with them, shortly, not efficiently. During the first experiment, the ants received the larva progressively inserted further into the tube, and interacted with the proto-tool more than during the two other experiments. Therefore, Myrmica rubra ants might be able to use some proto-tools following long-lasting habituation, imitation or conditioning processes, which would not be a strictly use of tools. Thus, ant cognition in this species extended up to but did not include the use of proto-tools, and at fortiori of sensu stricto tools.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.065
GPT teacher head0.342
Teacher spread0.277 · 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
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of BiologySame topicInsect and Arachnid Ecology and BehaviorFrench-language works237,207