Pollinator Diversity and Floral Calendar of Forage Resources for Pumpkin, Machakos County, Kenya
Bibliographic record
Abstract
The abundance and diversity of bees correlate with the abundance and diversity of forage resources. Pollinator reservoirs in croplands can augment pollination of flowering crops by increasing pollinator diversity and abundance. Therefore, this study provides empirical evidence on the diversity and abundance of alternative forage resources in two landscape classes with pumpkin fields. The study was undertaken in 32 pumpkin farms; 16 for each natural difference vegetation index (NDVI classes). Flowering plants were identified and counted within and around farms at 2 m × 2 m quadrat and 4 m × 50 m belt transect from the middle of the farm towards the exit. Approximately 142 plant species were recorded and their abundance varied between NDVI classes and across months. In the 2 m × 2 m quadrat 6,765 plants were observed in May in low NDVI and 4,399 in medium NDVI were recorded in May. Abundance, diversity and annual floral resource structure for honeybees in low and medium Normalized difference vegetation index (NDVI) classes in Machakos County were determined. The 4 m × 50 m belt transect had the highest numbers of plants (1,434-20,825) and plant species across the months and between NDVI classes. At least 35% of plants remained actively flowering during the sampling period, therefore, can serve as alternative forage sources for pollinators. Our inventory of plants can be used to develop a floral calendar for adoption by farmers for on-farm management of pollinators. The floral calendars will be predictive tools used to detect a correlation between flowering plants, seasons and pollinators.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".