Influence of Shape, Color, Size and Relative Position of Elements on Their Counting by an Ant
Bibliographic record
Abstract
It was previously shown that workers of the ant Myrmica sabuleti can discriminate small numbers of elements during testing when these elements were identical to those learned during training. Here we examine if this numerosity ability still subsists if the shape, color, size or location of the elements (dots) to count are modified between training and testing. We found that the ants’ counting ability was not significantly affected by changing one of these features although a somewhat lesser ability was observed. Among the changes, it was that of the relative position of the elements which disrupted the most the ants’ counting ability, followed by a change of their size. A change in the color or the shape affected the least the ants’ counting. These impacts of feature changes in the learned cues on the ants’ counting can be explained by characteristics of the visual perception of the species as well as by its behavioral ecology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".