What scatter-hoarding animals have taught us about small-scale navigation
Bibliographic record
Abstract
Many animals use cues for small-scale navigation, including beacons, landmarks, compasses and geometric properties. Scatter-hoarding animals are a unique system to study small-scale navigation. They have to remember and relocate many individual spatial locations, be fairly accurate in their searching and have to remember these locations for long stretches of time. In this article, we review what is known about cue use in both scatter-hoarding birds and rodents. We discuss the importance of local versus global cues, the encoding of bearings and geometric rules, the use of external compasses such as the Sun and the influence of the shape of experimental enclosures in relocating caches or hidden food. Scatter-hoarding animals are highly flexible in how and what they encode. There also appear to be differences in what scatter-hoarding birds and rodents encode, as well as what scatter-hoarding animals in general encode compared with other animals. Areas for future research with scatter-hoarding animals are discussed in light of what is currently known.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".