Data from: The evolution of floral sonication, a pollen foraging behavior used by bees (Anthophila)
Bibliographic record
Abstract
Over 22,000 species of biotically pollinated flowering plants, including some major agricultural crops, depend primarily on bees capable of floral sonication for pollination services. The ability to sonicate (“buzz”) flowers is widespread in bees but not ubiquitous. Despite the prevalence of this pollinator behavior and its importance to natural and agricultural systems, the evolutionary history of floral sonication in bees has not been previously studied. Here, we reconstruct the evolutionary history of floral sonication in bees by generating a time-calibrated phylogeny and reconstructing ancestral states for this pollen extraction behavior. We also test the hypothesis that the ability to sonicate flowers and thereby efficiently access pollen from a diverse assemblage of plant species, led to increased diversification amongst sonicating bee taxa. We find that floral sonication evolved on average 45 times within bees, possibly first during the Early Cretaceous (100-145 million years ago) in the common ancestor of bees. We find that sonicating lineages are significantly more species rich than non-sonicating sister lineages when comparing sister clades, but a probabilistic structured rate permutation on phylogenies approach failed to support the hypothesis that floral sonication is a key driver of bee diversification. This study provides the evolutionary framework needed to further study how floral sonication by bees may have facilitated the spread and common evolution of angiosperm species with poricidal floral morphology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.038 |
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".