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Record W4313545980 · doi:10.1128/spectrum.04180-22

Sweat and Sebum Preferences of the Human Skin Microbiota

2023· article· en· W4313545980 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrobiology Spectrum · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsMcMaster University
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institutes of Health
KeywordsBiologyMicrobiomeSWEATHuman skinEcologyMicrobial ecologyContext (archaeology)NicheMicrobiologyBacteriaZoologyBioinformaticsGenetics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.255
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it