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Record W3154535139 · doi:10.1016/j.xpro.2021.100435

Processing human urine and ureteral stents for 16S rRNA amplicon sequencing

2021· article· en· W3154535139 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSTAR Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsWestern UniversityLawson Health Research Institute
FundersW. Garfield Weston FoundationGarfield Weston Foundation
KeywordsAmpliconAmplicon sequencing16S ribosomal RNAUrineDNA extractionMicrobiomeStentBiologyComputational biologyPolymerase chain reactionMedicineBioinformaticsGeneGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Ureteral stents are commonly used medical devices that harbor a unique and patient-specific microbial community. This protocol describes an optimized procedure for high-quality DNA extraction from both urine and ureteral stent samples for the purpose of downstream microbiota characterization by amplicon sequencing. Detailed instruction is provided for 16S rRNA gene V4 region sequencing with the Illumina platform, which enables accurate and reproducible microbiota profiling of low bacterial abundance urine and stent samples. For complete details on the use and execution of this protocol, please refer to Al et al. (2020).

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 score0.524
Threshold uncertainty score0.540

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 studies0.0000.000
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 head0.083
GPT teacher head0.389
Teacher spread0.306 · 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