False-colour photography: a novel digital approach to visualize the bee view of flowers
Bibliographic record
Abstract
The colour vision system of bees and humans differs mainly in that, contrary to humans, bees are sensitive to ultraviolet light and insensitive to red light. The synopsis of a colour picture and a UV picture is inappropriate to illustrate the bee view of flowers, since the colour picture does not exclude red light. In this study false-colour pictures in bee view are assembled from digital photos taken through a UV, a blue, and a green filter matching the spectral sensitivity of the bees’ photoreceptors. False-colour pictures demonstrate small-sized colour patterns in flowers, e.g. based on pollen grains, anthers, filamental hairs, and other tiny structures that are inaccessible to spectrophotometry. Moreover, false-colour pictures are suited to demonstrate flowers and floral parts that are conspicuous or inconspicuous to bees. False-colour pictures also direct the attention to other ranges of wavelength besides ultraviolet demonstrating for example blue and yellow bulls’ eyes in addition to UV bulls’ eyes which previously have been overlooked. False-colour photography is a robust method that can be used under field conditions, with various equipment and with simple colour editing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".