Bee visit rates vary with floral morphology among highbush blueberry cultivars (<i><scp>V</scp>accinium corymbosum </i><scp>L</scp>.)
Bibliographic record
Abstract
Abstract As the human population has increased, so too has the demand for biotically pollinated crops. Bees (Apoidea) are essential for pollen transfer and fruit production in many crops, and their visit patterns can be influenced by floral morphology. Here, we considered the role of floral morphology on visit rates and behaviour of managed honey bees (Apis mellifera) and wild bumble bees (genus Bombus), for four highbush blueberry cultivars (Vaccinium corymbosum L.). We measured five floral traits for each cultivar, finding significant variation among cultivars. Corolla throat diameter may be the main morphological determinant of visit rates of honey bees, which is significantly higher on the wider flowers of cv. ‘Duke’ than on ‘Bluecrop’ or ‘Draper’. Honey bees also visited cv. ‘Duke’ legitimately but were frequent nectar robbers on the long, narrow flowers of cv. ‘Bluecrop’. Bumble bees were infrequent (and absent on cv. ‘Draper’) but all observed visits were legitimate. Crop yield was highest for the cultivar with the highest combined (honey bee + bumble bee) visit rate, suggesting that aspects of floral morphology that affect pollinator visit patterns should be considered in crop breeding initiatives.
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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) | 0.001 | 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".