When Associating Numbers of Elements With Their Time Period of Occurrence, the Ants Take Account of the Characteristics of the Elements
Bibliographic record
Abstract
After having shown that the workers of the ant Myrmica sabuleti can associate amounts of elements with their time periods of occurrence and knowing that these ants do not take into account the characteristics of elements when counting but take them into account when adding the elements, we wondered if, when associating amounts with their time periods of occurrence, these ants take or do not take account of the characteristics of the elements. Working on six colonies and using three kinds of visual cues during training and these three cues modified as for their size (small, large), color (blue, yellow) or shape (triangle, star) during testing, we revealed that, when associating amounts of elements with their time periods of occurrence, the ants take into account the characteristics of the elements. We checked if, without changing the elements characteristics, the ants effectively associated the perceived amounts (1 – 3, 2 – 4, 3 -5) of elements (squares, blue circles, triangles) with their time periods of occurrence (8 – 19 o’clock, 20 – 7 o’clock), and they did. We also made a complementary experiment on newly collected colonies using a slightly different protocol, and we obtained identical results which leaded to the same conclusion. So, the present work confirmed our previous results and solved the last asked question on the subject.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".