Behaviourally mediated crypsis in two nocturnal moths with contrasting appearance
Bibliographic record
Abstract
The natural resting orientations of several species of nocturnal moth on tree trunks were recorded over a three-month period in eastern Ontario, Canada. Moths from certain genera exhibited resting orientation distributions that differed significantly from random, whereas others did not. In particular, Catocala spp. collectively tended to orient vertically, whereas subfamily Larentiinae representatives showed a variety of orientations that did not differ significantly from random. To understand why different moth species adopted different orientations, we presented human subjects with a computer-based detection task of finding and 'attacking' Catocala cerogama and Euphyia intermediata target images at different orientations when superimposed on images of sugar maple (Acer saccharum) trees. For both C. cerogama and E. intermediata, orientation had a significant effect on survivorship, although the effect was more pronounced in C. cerogama. When the tree background images were flipped horizontally the optimal orientation changed accordingly, indicating that the detection rates were dependent on the interaction between certain directional appearance features of the moth and its background. Collectively, our results suggest that the contrasting wing patterns of the moths are involved in background matching, and that the moths are able to improve their crypsis through appropriate behavioural orientation.
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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 | 0.000 | 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) | 0.001 | 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".