Approach trajectory and solar position affect host plant attractiveness to the small white butterfly
Bibliographic record
Abstract
While it is well documented that insects exploit polarized sky light for navigation, their use of reflected polarized light for object detection has been less well studied. Recently, we have shown that the small white butterfly, Pieris rapae, distinguishes between host and non-host plants based on the degree of linear polarization (DoLP) of light reflected from their leaves. To determine how polarized light cues affect host plant foraging by female P. rapae across their entire visual range including the ultraviolet (300-650 nm), we applied photo polarimetry demonstrating large differences in the DoLP of leaf-reflected light among plant species generally and between host and non-host plants specifically. As polarized light cues are directionally dependent, we also tested, and modelled, the effect of approach trajectory on the polarization of plant-reflected light and the resulting attractiveness to P. rapae. Using photo polarimetry measurements of plants under a range of light source and observer positions, we reveal several distinct effects when polarized reflections are examined on a whole-plant basis rather than at the scale of pixels or plant canopies. Most notably from our modeling, certain approach trajectories are optimal for foraging butterflies, or insects generally, to discriminate between plant species on the basis of the DoLP of leaf-reflected light.
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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) | 0.003 | 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".