Conical flower cells reduce surface gloss and improve colour signal integrity for free-flying bumblebees
Bibliographic record
Abstract
Colour signals of flowers facilitate detection, spontaneous preference, discrimination and flower constancy by important bee pollinators. At short distances bees orient to floral colour patterns to find a landing platform and collect nutrition, potentially improving the plants’ reproductive success when multiple flowers are visited sequentially. In addition to pigments and backscattering structures within the petals’ internal layers, the epidermal micro-structure of the petals’ surface may also influence petal reflectance properties and thus influence overall colour patterns via optical effects. Gloss, i.e., shine caused by specular reflections of incident light from smooth surfaces, may for example alter the visual appearance of surfaces including flowers. We classify the epidermal surface properties of petals from 39 species of flowering plants from 19 families by means of a cell shape index, and measure the respective surface spectral reflectance from different angles. The spontaneous behavioural preferences of free flying bumblebees (Bombus terrestris) for surfaces with different micro-textures was then tested using specially prepared casts of selected flower petals. We specifically tested how the petal colour as function of the angle of incident light, surface structure and bee approach angle influences bumblebees’ spontaneous choices for artificial flowers. We observe that bumblebees spontaneously prefer artificial flowers with conical-papillate micro-structures under both multidirectional illumination and under spotlight conditions if approaching against the direction of spotlight, suggesting conical cells help promote constant signals by removing gloss that may confound the integrity of colour signalling.
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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) | 0.002 | 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".