Visual Matching in the Orientation of Desert Ants (<i><scp>M</scp>elophorus bagoti</i>): The Effect of Changing Skyline Height
Bibliographic record
Abstract
Abstract Ants are known to use the terrestrial visual panorama in navigation. Recent evidence has accumulated for the use of the currently perceived visual panorama to determine a direction to head in. The pattern of the height of the terrestrial surround, the skyline, is one key cue for the C entral A ustralian red honey ant M elophorus bagoti in determining a direction of travel. But ants might also possess some mechanism to match the skyline heights encountered during training, which functions to steer away from regions whose skyline is too high and towards regions whose skyline is too low. We made an initial test of this hypothesis by training ants to visit a feeder centred between two experimentally constructed walls of black cloth. Trained ants were then tested for their initial homing direction with the walls retaining their heights as encountered in training (controls), with one of the walls lowered or raised in height, or with one wall lowered and the opposite wall raised. Wall‐height manipulations deflected the initial headings of ants towards the lower wall, with combined wall lowering and wall raising changing the initial headings by ~30° when compared with controls. The results suggest that the ants combined the dictates of the panorama in determining the best direction of travel (a heading towards the nest) with some attractor mechanism that functions to establish the skyline heights of training conditions (a heading towards the lower wall).
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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 | 0.001 | 0.000 |
| 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".