Orchid bees enhance seed set production of an understory herb in the Western Brazilian Amazon
Bibliographic record
Abstract
Bee pollination is an important ecosystem service related to the maintenance of many flowering plants. We evaluated the relationship between orchid bee foraging time and the density of flowering plants and whether visitation varied according to the sex and size class of bees, using Calathea mansonis as a model species. We monitored 10 plots between December 2009 and November 2010 in a forest fragment in Senador Guiomard, Acre, Brazil. We counted the number of flowering plants and flowers per plant and the behaviour of the observed bees. Additionally, we compared the bagged and exposed inflorescences for self-compatibility analysis. We sampled 173 orchid bees from 13 species, with Eulaema cingulata as the most abundant visitor. Eulaema (large bees) were more effective pollinators than Euglossa (small bees). We also found Eulaema polyzona individuals feeding on a Marantaceae species for the first time. The time spent by the bees visiting flowers did not differ with the density of flowering plants or the number of flowers per plant. However, flowers exposed to visitors produced 35% more seeds and 15% heavier seeds than bagged flowers. Considering plant–bee interactions, orchid bees may increase gene flow and compensate for the clonal reproduction of this herb.
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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".