The BeeBiome data portal provides easy access to bee microbiome information
Bibliographic record
Abstract
Bees can be colonized by a large diversity of microbes, including beneficial gut symbionts and detrimental pathogens, with implications for bee health. Over the last few years, researchers around the world have collected a huge amount of genomic and transcriptomic data about the composition, genomic content, and gene expression of bee-associated microbial communities. While each of these datasets by itself has provided important insights, the integration of such datasets provides an unprecedented opportunity to obtain a global picture of the microbes associated with bees and their link to bee health. The challenge of such an approach is that datasets are difficult to find within large generalist repositories and are often not readily accessible, which hinders integrative analyses. Here we present a publicly-available online resource, the BeeBiome data portal ( https://www.beebiome.org ), which provides an overview of and easy access to currently available metagenomic datasets involving bee-associated microbes. Currently the data portal contains 33,678 Sequence Read Archive (SRA) experiments for 278 Apoidea hosts. We present the content and functionalities of this portal. By providing access to all bee microbiomes in a single place, with easy filtering on relevant criteria, BeeBiome will allow faster progress of applied and fundamental research on bee biology and health. It should be a useful tool for researchers, academics, funding agencies, and governments, with beneficial impacts for stakeholders.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".