The case for microbiome stewardship: what it is and how to get there
Bibliographic record
Abstract
Microbiomes are essential for human, animal, plant, and ecosystem health. Despite widespread recognition of the importance of microbiomes, there is little attention paid to monitoring and safeguarding microbial ecologies on policy levels. We observe that microbiomes are deteriorating owing to practices at societal levels such as pesticide use in agriculture, air and water pollution, and overuse of antibiotics. Potential policy on these issues would cross multiple domains such as public health, environmental protection, and agriculture. We propose microbiome stewardship as a foundational concept that can act across policy domains to facilitate healthy microbiomes for human and ecosystem health. We examine challenges to be addressed and steps to take toward developing meaningful microbiome stewardship.
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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".