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Record W6931931760 · doi:10.5683/sp3/g2du0q

Supplemental data for: Tracking the microbial communities from the farm to the processing facility of a washed-rind cheese operation

2024· dataset· en· W6931931760 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

VenueBorealis · 2024
Typedataset
Languageen
FieldComputer Science
TopicGraph Theory and Algorithms
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAmplicon sequencingMilkingMicrobiome16S ribosomal RNARaw milkMetagenomicsUniFracWater quality

Abstract

fetched live from OpenAlex

Milk residue and the accompanying biofilm accumulation in milking systems can compromise the microbial quality of milk and the downstream processes of cheese production. Over a six-month study, the microbial ecosystems of milk, tap water and environmental swabs were cultured and sequenced to view the shared microbiota between the farm and the processing facility. Culture independent analysis of bacteria in milk, water and swab samples revealed a shared microbiota between the sample types of both facilities after sanitation. Amplicon sequence variants (ASVs) of the V3-V4 region of the 16S rRNA gene revealed that milk samples have a lower diversity than water or environmental swabs. Brevibacterium and Yaniella (both Actinobacteria) were observed in all sampling types. Further studies will include whole genome sequencing of Brevibacterium spp. isolates to determine their functionality and diversity within the system.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.081
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0070.002
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 head0.055
GPT teacher head0.303
Teacher spread0.249 · 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