Factors determining visual detection distance to real flowers by bumble bees
Bibliographic record
Abstract
Plants use visual signals to attract pollinators and direct them to their flowers. Visual capabilities of bees have been extensively studied mostly using artificial paper models. However, there is no empirical determination of the maximal detection distance (MDD) or minimal subtended visual angle (MSVA) of real flowers. Using a six armed radial maze, we tested MDD and MSVA of 12 types of natural and manipulated real flowers by bumble bee (Bombus terrestris) workers. Bees were initially trained to obtain sugar solution at target flowers that were presented at close range on a mobile divider at the back of one of the six arms. Bees were individually marked and tested. For bees that passed the short range test, we gradually increased the distance of the target flower, until the number of successful choices reached chance level, indicating that they could not see the target flowers. The results show that MSVA of flowers is correlated with flower diameter but not with MDD. The variation in MDD to natural flowers by bumble bee workers can be best predicted by: MDD = flower coloured area / (contour line * green contrast). Contour line length determines flower dissectedness. Full circular flowers can be detected from longer distance than dissected flowers with identical diameter. We hypothesize that dissected flower shapes might be compensated by their higher attractiveness for bees. Empirical determination of real flower MDD and MSVA is important for studying bee foraging behaviour, pollinator induced evolution of flower traits and validation of neurophysiological visual models.
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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 | 0.002 |
| 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 | 0.001 | 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".