A blueprint for contemporary studies of microbiomes
Bibliographic record
Abstract
This editorial piece co-authored by the Senior Editors at Microbiome aims to highlight current challenges in the field of environmental and host-associated microbiome research. We also take the opportunity to clarify our expectations for the articles submitted to the journal. At Microbiome, we are seeking studies that provide either new mechanistic insights into the role of microbiomes in health and environmental systems or substantial conceptual or technical advances. Manuscripts need to meet high standards of language accuracy, quality of microbiome analyses, and data and protocol availability, including detailed reporting of wet-lab and in silico protocols, all of which can critically enhance transparency and reproducibility. We think that such efforts are essential to push the boundaries of our knowledge on microbiomes in a concerted, international effort.
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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".