Bee interactions with wild flora around organic and conventional coffee farms in Kiambu district, central Kenya
Bibliographic record
Abstract
Flower scarcity outside coffee flowering periods leads to a decline of pollinators' abundance and diversity possibly through death or migration. The objective of this study was to assess whether other flowering plants within and around coffee farms act as alternative floral resources that may impact on abundance and diversity of pollinators of coffee flowers. Bee pollinators of coffee were assessed and identified for a period of 27 months. Their abundance and diversity were examined within and around organically and conventionally managed coffee farms in Kiambu District in Kenya. This study provides evidence that 42 plant species from 19 families were alternative floral resources for bees that pollinate coffee. Bee pollinators of coffee were observed to visit coffee flowers as well as other flowering plants close by. Significant relationship existed between plant species and bee species richness in the organic farming (R2=0.5918; P<0.0001) and in conventional farming (R2=0.6744; P<0.0001). Therefore in coffee monocultures, presence of other flowering plants should be encouraged to support bee pollinators when coffee is not flowering and to enhance abundance and diversity of bees visiting coffee flowers.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".