Information content of visual scenes influences systematic search of desert ants
Bibliographic record
Abstract
Many animals - including insects - navigate visually through their environment. Solitary foraging desert ants are known to acquire visual information from the surrounding panorama and use it to navigate along habitual routes or to pinpoint a goal such as the nest. Returning foragers that fail to find the nest entrance engage in searching behaviour, during which they continue to use vision. The characteristics of searching behaviour have typically been investigated in unfamiliar environments. Here we investigated in detail the nest-searching behaviour of Melophorus bagoti foragers within the familiar visual environment of their nest. First, by relating search behaviour to the information content of panoramic (360 deg) images, we found that searches were more accurate in visually cluttered environments. Second, as observed in unfamiliar visual surrounds, searches were dynamic and gradually expanded with time, showing that nest pinpointing is not rigidly controlled by vision. Third, contrary to searches displayed in unfamiliar environments, searches observed here could be modelled as a single exponential search strategy, which is similar to a Brownian walk, and there was no evidence of a Lévy walk. Overall, our results revealed that searching behaviour is remarkably flexible and varies according to the relevance of information provided by the surrounding visual scenery.
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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".