A new lexicon in the age of microbiome research
Bibliographic record
Abstract
At a rapid pace, biologists are learning the many ways in which resident microbes influence, and sometimes even control, their hosts to shape both health and disease. Understanding the biochemistry behind these interactions promises to reveal completely novel and targeted ways of counteracting disease processes. However, in our protocols and publications, we continue to describe these new results using a language that originated in a completely different context. This language developed when microbial interactions with hosts were perceived to be primarily pathogenic, as threats that had to be vanquished. Biomedicine had one dominating thought: winning this war against microorganisms. Today, we know that beyond their defensive roles, host tissues, especially epithelia, are vital to ensuring association with the normal microbiota, the communities of microbes that persistently live with the host. Thus, we need to adopt a language that better encompasses the newly appreciated importance of host-microbiota associations. We also need a language that frames the onset and progression of pathogenic conditions within the context of the normal microbiota. Such a reimagined lexicon should make it clear, from the very nature of its words, that microorganisms are primarily vital to our health, and only more rarely the cause of disease. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".