Beekeeping Genetic Resources and Retrieval of Honey Bee Apis mellifera L. Stock in the Russian Federation: A Review
Bibliographic record
Abstract
The loss of honey bees has drawn a large amount of attention in various countries. Therefore, the development of efficient methods for recovering honey bee populations has been a priority for beekeepers. Here we present an extended literature review and report on personal communications relating to the characterization of the local and bred stock of honey bees in the Russian Federation. New types have been bred from local colonies (A. mellifera L., A. m. carpatica Avet., A. m. caucasia Gorb.). The main selection traits consist of a strong ability for overwintering, disease resistance and different aptitudes for nectar collection in low and high blooming seasons. These honey bees were certified by several methods: behavioral, morphometric and genetic analysis. We illustrate the practical experience of scientists, beekeepers and breeders in breeding A. mellifera Far East honey bees with Varroa and tracheal mite resistance, which were the initial reasons for breeding the A. mellifera Far Eastern breed by Russian breeders, Russian honey bee in America, the hybrid honey bee in Canada by American breeders, and in China by Chinese beekeepers. The recent achievements of Russian beekeepers may lead to the recovery of beekeeping areas suffering from crossbreeding and losses of honey bee colonies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".