Annual diversity of honeybee pollen sources in two pumpkin growing landscapes, Machakos County, Kenya
Bibliographic record
Abstract
Multi-floral foraging sources for honey bee (Apis mellifera L.) have been threatened by landscape changes and unsustainable farming practices. In East Africa, the biodiversity of forage resources that could support honey bees, especially in agricultural lands, remains least explored. This study investigated pollen diversity for honey bees in Yatta and Masinga Sub-counties in Machakos County, Kenya. Honey bee hives were installed on eight pumpkin (Cucurbita maxima Duchesne ex Lam) farms (one hive per farm) in two varying landscape vegetation classes (low and medium) based on normalized difference vegetation index (NDVI). Pollen traps were installed at the hive entrance and pollen pellets were retrieved fortnightly for subsequent identification. Approximately 115 pollen types were identified. The pollen pellets were associated with 63 plant families and 109 plant families in low and medium NDVI classes, respectively. The predominant source of pollen for honey bees in low NDVI class included the plant families Poaceae (5,526 pellets), Asteraceae (3,176 pellets), Combretaceae (1,327 pellets), Acanthaceae (1,122 pellets), Amaranthaceae (960 pellets), Boraginaceae (951 pellets), Typhaceae (889 pellets), Guaduelleae (809 pellets) and Loranthaceae (588 pellets). In medium NDVI, most pollen was sourced from plant family Asteraceae (3,605 pellets), Malvaceae (1,572 pellets), Poaceae (1,538 pellets), Combretaceae (1,468 pellets), Salvadoraceae (1,354 pellets), Solanaceae (1,011 pellets), Asphodelaceae (885 pellets), Fabaceae (882 pellets), Euphorbiaceae (685 pellets) and Myrtaceae (546 pellets). The diversity of pollen types varied between the NDVI classes and across months. Although honey bees sourced diverse pollen required for the dietary balance of the colony, there was variability across landscape types and months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".