Bibliographic record
Abstract
Perhaps now more than ever (1), it is abundantly clear that viruses can rapidly and dramatically alter host populations, both by direct mortality and by changing the way hosts interact with each other. Like macroscopic organisms, bacteria also contend with their own viruses. Called bacteriophages (or simply, phages), these nanometer-scale parasites are the most numerous, yet least well-characterized, forms of “life” on the planet (2, 3). Because of their inability to directly grow phages (or most of their bacterial hosts), researchers have had only a limited view of the abundance, distribution, and population structure of phage communities. Now, a pair of studies in PNAS, by Bonilla-Rosso et al. (4) and Deboutte et al. (5), have pulled back the curtain on the phages of an emerging model microbiome system: that of the bee gut. Honey bees ( Apis mellifera ), like humans, have a uniquely specialized gut microbiome that has developed over millions of years of coevolution (6). A billion cells strong, the bacteria within each bee play significant roles in extracting dietary nutrients and fending off pathogens (7⇓⇓⇓–11). If what we know from mammalian gut microbiomes holds (12), there also exist as many phages as bacteria in the bee gut. However, to date, there has been no unbiased systematic survey of phages from bees. Both Bonilla-Rosso et al. (4) and Deboutte et al. (5) ambitiously set out to use metagenomic sequencing on viral particles purified from bees, to offer a glimpse into the diversity of phages present as well as the types of genes they carry. Bonilla-Rosso et al. (4) combined hundreds of bees from Swiss hives into two samples for analysis, whereas … [↵][1]1Email: waldankwong{at}gmail.com. [1]: #xref-corresp-1-1
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".