Self-tracking the microbiome: where do we go from here?
Bibliographic record
Abstract
The quantified self community brings together enthusiasts who are using technological devices to monitor their health and social media to share their personal data with others online. In light of the growing popularity of this movement, self-trackers are challenging the health-care system by raising important questions about data ownership and risk-taking. As we enter a new era of consumer genomics, a significant number of quantified self (QS) individuals are now interested in the monitoring of their microbiome and performing personal interventions. In this paper, we discuss the scientific validity of experiments involving serial observations of a single individual as opposed to randomized clinical trials. We look at self-tracking from an ethical standpoint by questioning the risks and assessing the potential benefits for personalized medicine in general and for microbiome research in particular.
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 | 0.079 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".