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Record W2970489378 · doi:10.5539/ijb.v11n4p51

Subtraction-Like Effect in an Ant Faced with Numbers of Elements Including a Crossed One

2019· article· en· W2970489378 on OpenAlexvenueno aff
Marie‐Claire Cammaerts, Roger Cammaerts

Bibliographic record

VenueInternational Journal of Biology · 2019
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
Fundersnot available
KeywordsColoredElement (criminal law)SubtractionMathematicsArithmeticCommunicationCombinatoricsPsychologyMaterials science

Abstract

fetched live from OpenAlex

When trained to a number of colored elements of which one was crossed, tested Myrmica sabuleti worker ants reacted essentially to that number of elements excluding the crossed one. They thus apparently subtracted the crossed element, moreover doing so better for fewer numbers of elements. When trained in the same way but tested in front of a number of uncrossed elements equal to the total number of crossed and uncrossed elements seen during training, and at the same time, to the same number of elements without the crossed one as well as to the crossed element only, the ants again reacted essentially to the number of elements minus the crossed one. Again, they did so better for smaller numbers. The ants reacted thus to the visual cue they best saw during training. They also somewhat reacted to the isolated crossed element and better when trained to lower numbers of elements. When trained to a number of uncrossed colored elements and at the same time, to a cue showing the same number of elements, but one crossed and the others uncolored, the tested ants reacted more to the initial total number of colored elements than to the same total number of colored elements minus the crossed one. This showed again that the ants did not really subtract, but reacted to what they the most distinctly saw during training. They also again better reacted in presence of fewer elements.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.416
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.037
GPT teacher head0.371
Teacher spread0.334 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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