Sweat and Sebum Preferences of the Human Skin Microbiota
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The human skin microbiome is adapted to survive and thrive in the harsh environment of the skin, which is low in nutrient availability. To study skin microorganisms in a system that mimics the natural skin environment, we developed and tested a physiologically relevant, synthetic skin-like growth medium that is composed of compounds found in the human skin secretions sweat and sebum. We find that most skin-associated bacterial species tested prefer high concentrations of artificial sweat but that artificial sebum concentration preference varies from species to species, suggesting that sebum utilization may be an important contributor to skin microbiome composition. This study demonstrates the utility of a skin-like growth medium, which can be applied to diverse microbiological systems, and underscores the importance of studying microorganisms in an ecologically relevant context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it