Traveling through light clutter: Path integration and panorama guided navigation in the Sonoran Desert ant, Novomessor cockerelli
Bibliographic record
Abstract
Foraging ants use multiple navigational strategies, including path integration and visual panorama cues, which are used simultaneously and weighted based upon context, the environment and the species’ sensory ecology. In particular, the amount of visual clutter in the habitat predicts the weighting given to the forager’s path integrator and surrounding panorama cues. Here, we characterize the individual cue use and cue weighting of the Sonoran Desert ant, Novomessor cockerelli, by testing foragers after local and distant displacement. Foragers attend to both a path-integration-based vector and the surrounding panorama to navigate, on and off foraging routes. When both cues were present, foragers initially oriented to their path integrator alone, yet weighting was dynamic, with foragers abandoning the vector and switching to panorama-based navigation after a few meters. If displaced to unfamiliar locations, experienced foragers travelled almost their full homeward vector (~85%) before the onset of search. Through panorama analysis, we show views acquired on-route provide sufficient information for orientation over only short distances, with rapid parallel decreases in panorama similarity and navigational performance after even small local displacements. These findings are consistent with heavy path integrator weighting over the panorama when the local habitat contains few prominent terrestrial cues.
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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 | 0.000 | 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 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".