Subtraction-Like Effect in an Ant Faced with Numbers of Elements Including a Crossed One
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".