Stingless bees further improve apple pollination and production
Bibliographic record
Abstract
The use of Africanised honeybee (Apis mellifera scutellata Lepeletier) hives to increase pollination success in apple orchards is a widespread practice. However, this study is the first to investigate the number of honeybee hives ha-1 required to increase the production of fruits and seeds as well as the potential contribution of the stingless bee Mandaçaia (Melipona quadrifasciata anthidioides Lepeletier). We performed tests in a 43-ha apple orchard located in the municipality of Ibicoara (13º24’50.7’’S and 41º17’7.4’’W) in Chapada Diamantina, State of Bahia, Brazil. In 2011, fruits from the Eva variety set six seeds on average, and neither a greater number of hives (from 7 to 11 hives ha-1) nor a greater number of pollen collectors at the honeybee hives displayed general effects on the seed number. Without wild pollinators, seven Africanised honeybee hives ha-1 with pollen collectors is currently the best option for apple producers because no further increase in the seed number was observed with higher hive densities. In 2012, supplementation with both stingless bees (12 hives ha-1) and Africanised honeybees (7 hives ha-1) provided higher seed and fruit production than supplementation with honeybees (7 hives ha-1) alone. Therefore, the stingless bee can improve the performance of honeybee as a pollinator of apple flowers, since the presence of both of these bees results in increases in apple fruit and seed number.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".