Standard methods and good practices in <i>Apis</i> honey bee omics research
Bibliographic record
Abstract
In the past decades, COLOSS members have joined forces multiple times to develop and condense standard methods related to research on honey bees, their pests, pathogens, and colony products. This led to the publication of four open-access BEEBOOK volumes that have been utilized by researchers worldwide. Among the chapters, “Standard methods for molecular research in Apis mellifera,” written by Evans and collaborators in 2013, has been a cornerstone for the standardization of honey bee molecular studies. However, since sequencing technologies and analyzing algorithms have made tremendous progress, many described methods require updating. In parallel, other Apis species’ genomes have now been sequenced, thus opening new research avenues in a comparative framework. In this chapter, we add to the methods previously covered by Evans et al. in 2013 and provide updated methodology where necessary, including worked examples and bioinformatic analysis pipelines. We also cover topics which were not previously covered in depth, such as sequencing ancient samples, population genomics, proteomics, and sampling honey bee colony products for microbiome studies, among others. Our hope is for this to become a lasting resource for honey bee scientists as the field continues to advance.
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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 | 0.079 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.031 |
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".