Capability of Workers of the Ant Myrmica sabuleti to Categorize Numbers of Elements into Even and Odd
Bibliographic record
Abstract
Categorizing numbers into even and odd is an ability held by humans that has recently been found to be also held by honeybees. We examined whether ants could also make such parity discrimination. Working on the species Myrmica sabuleti, we learned the ants of two colonies to associate 2 black circles with a reward and 5 of these circles with the absence of a reward, as well as learned the ants of two other colonies to associate 3 of these same cues with a reward and 4 of these cues with the absence of a reward. By collectively testing foragers of each colony in a separate tray in front of these cues, it was first verified during three days if they dully learned the ‘correct’ cue. Thereafter, while the ants continued to be trained, foragers of each colony were collectively subjected to nine successive choice tests, each day in front of a pair of cues different from the one used during the learning process. The cues used during these tests differed from those used to train the ants by the number and size of the dots, the cumulative surface of the dots, the perimeter of their area (surfaces and perimeters being maintained equal between the cues) and their layout. A ‘correct’ cue had the same parity (even or odd) as the cue learned during training. The tested ants each time consistently responded to the number that had the same parity as that of the number they learned during conditioning. This sensitivity to the number parity occurred from the number 1 until the number 7. Discrimination between 7 and 8 dots was beyond the ant capability.
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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) | 0.000 | 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) | 0.001 | 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".