From larva to adult: In vitro rearing protocol for honey bee (Apis mellifera) drones
Bibliographic record
Abstract
Development of a successful in vitro rearing protocol has been essential for pesticide safety assessment of immature honey bee workers under laboratory conditions. In contrast, pesticide safety testing of honey bee drones is limited, in part due to the lack of successful laboratory rearing protocols for this reproductive caste. Considering that healthy drones are essential for successful mating and reproduction of the honey bee queen, a standardized in vitro rearing protocol for honey bee drones is necessary to support reproductive safety studies, as well as to gain a deeper understanding of honey bee drone development. Using the established in vitro rearing protocol for honey bee workers, we modified the days of grafting and pupal transfer, as well as the diet volume, pupation plate orientation, and absorbent tissue in the pupal wells to successfully rear honey bee drones in vitro. In vitro-reared drones were evaluated for gross wing abnormalities, body weight, testes weight, and abdominal area, and compared with age-matched drones reared in field colonies. We found that honey bee drones reared in a vertically oriented pupation plate containing WypAll® absorbent tissue in each well had a mean survival to adulthood of 74 ± 3.5% (SEM) until adulthood. In contrast, drones reared in a horizontally oriented pupation plate containing Kimwipe® absorbent tissue in each well had significantly lower survival (5.5 ± 2.3%) and demonstrated gross wing abnormalities. All in vitro-reared drones had significantly lower body weight, testes weight and abdominal area relative to colony-reared control drones. Accordingly, we successfully developed an in vitro rearing protocol for honey bee drones which has the potential to improve future reproductive safety assessment of pesticides for honey bees.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".