Combining sky and Earth: Desert ants (<i>Melophorus bagoti</i>) show weighted integration of celestial and terrestrial cues
Bibliographic record
Abstract
Insects typically use celestial sources of directional information for path integration, and terrestrial panoramic information for view-based navigation. Here we set celestial and terrestrial sources of directional information in conflict for homing desert ants (Melophorus bagoti). In the first experiment, ants learned to navigate out of a round experimental arena with a distinctive artificial panorama. On crucial tests, we rotated the arena to create a conflict between the artificial panorama and celestial information. In a second experiment, ants at a feeder in their natural visually-cluttered habitat were displaced prior to their homing journey so that the dictates of path integration (feeder to nest direction) based on a celestial compass conflicted with the dictates of view-based navigation (release point to nest direction) based on the natural terrestrial panorama. In both experiments, ants generally headed in a direction intermediate to the dictates of celestial and terrestrial information. In the second experiment, the ants put more weight on the terrestrial cues when they provided better directional information. We conclude that desert ants weight and integrate the dictates of celestial and terrestrial information in determining their initial heading, even when the two directional cues are highly discrepant.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".