The chemical ecology and evolution of bee–flower interactions: a review and perspectivesThe present review is one in the special series of reviews on animal–plant interactions.
Bibliographic record
Abstract
Bees and angiosperms have shared a long and intertwined evolutionary history and their interactions have resulted in remarkable adaptations. Yet, at a time when the “pollination crisis” is of major concern as natural populations of both wild and honey bees ( Apis mellifera L., 1758) face alarming decline rates at a worldwide scale, there are important gaps in our understanding of the ecology and evolution of bee–flower interactions. In this review, we summarize and discuss the current knowledge about the role of floral chemistry versus other communication channels in bee-pollinated flowering plants, both at the macro- and micro-evolutionary levels, and across the specialization–generalization gradient. The available data illustrate that floral scents and floral chemistry have been largely overlooked in bee–flower interactions, and that pollination studies integrating these components along with pollinator behaviour in a phylogenetic context will help gain considerable insights into the sensory ecology and the evolution of bees and their associated flowering plants.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".