Effect of Different Substrates on the Acceptance of Grafted Larvae in Commercial Honey Bee (Apis Mellifera) Queen Rearing
Bibliographic record
Abstract
Abstract The need for the increased production of honey bee (Apis mellifera) queens has led beekeepers to use different substrates in artificial queen cups where larvae destined to become queens are deposited (grafting). However, not enough scientific evidence exists that indicates that this practice is useful and what substance offers the best results. This study was conducted to determine with the Doolittle queen rearing method the acceptance rate of larvae deposited on different substrates during grafting and to determine if the sugar content and pH of the substrates used affect the acceptance of larvae in cell builder colonies. The evaluated substrates were coconut water, apple nectar, royal jelly, cola soda and distilled water, plus control (without substrate). Grafted larvae of the six treatments were introduced into cell builder colonies and their acceptance verified after 72 h. Apple nectar provided the highest rate of larvae acceptance with 81.06%, followed by cola soda with 62.93%, coconut water with 60.90%, royal jelly with 57.82% and distilled water with 58.99%. The larvae acceptance rates of all substrates were significantly higher than the control, which had an acceptance rate of 47.04%. No significant relationship was found between the sugar content of the substrates and larvae acceptance. However, although not significant, a high negative correlation was found between the substrate pH and the number of accepted larvae (Rho = - 0.90, p = 0.07). These results suggest that the use of liquid acidic substrates during larvae grafting, in particular apple nectar, may increase the production of honey bee queens.
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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 | 0.001 |
| 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 | 0.001 |
| 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".