Zebra Stripes through the Eyes of Their Predators, Zebras, and Humans
Bibliographic record
Abstract
The century-old idea that stripes make zebras cryptic to large carnivores has never been examined systematically. We evaluated this hypothesis by passing digital images of zebras through species-specific spatial and colour filters to simulate their appearance for the visual systems of zebras' primary predators and zebras themselves. We also measured stripe widths and luminance contrast to estimate the maximum distances from which lions, spotted hyaenas, and zebras can resolve stripes. We found that beyond ca. 50 m (daylight) and 30 m (twilight) zebra stripes are difficult for the estimated visual systems of large carnivores to resolve, but not humans. On moonless nights, stripes are difficult for all species to resolve beyond ca. 9 m. In open treeless habitats where zebras spend most time, zebras are as clearly identified by the lion visual system as are similar-sized ungulates, suggesting that stripes cannot confer crypsis by disrupting the zebra's outline. Stripes confer a minor advantage over solid pelage in masking body shape in woodlands, but the effect is stronger for humans than for predators. Zebras appear to be less able than humans to resolve stripes although they are better than their chief predators. In conclusion, compared to the uniform pelage of other sympatric herbivores it appears highly unlikely that stripes are a form of anti-predator camouflage.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".