Microbiome ethics, guiding principles for microbiome research, use and knowledge management
Bibliographic record
Abstract
The overarching biological impact of microbiomes on their hosts, and more generally their environment, reflects the co-evolution of a mutualistic symbiosis, generating fitness for both. Knowledge of microbiomes, their systemic role, interactions, and impact grows exponentially. When a research field of importance for planetary health evolves so rapidly, it is essential to consider it from an ethical holistic perspective. However, to date, the topic of microbiome ethics has received relatively little attention considering its importance. Here, ethical analysis of microbiome research, innovation, use, and potential impact is structured around the four cornerstone principles of ethics: Do Good; Don't Harm; Respect; Act Justly. This simple, but not simplistic approach allows ethical issues to be communicative and operational. The essence of the paper is captured in a set of eleven microbiome ethics recommendations, e.g., proposing gut microbiome status as common global heritage, similar to the internationally agreed status of major food crops.
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 | 0.033 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.044 | 0.060 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".