Standard methods and good practices in <i>Apis</i> honey bee omics research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it