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Record W4388462331 · doi:10.3389/fmicb.2023.1175663

Phylogenetic variation in raw cow milk microbiota and the impact of forage combinations and use of silage inoculants

2023· article· en· W4388462331 on OpenAlexafffund
Alexandre J. Kennang Ouamba, Mérilie Gagnon, Thibault Varin, P.Y. Chouinard, Gisèle LaPointe, Denis Roy

Bibliographic record

VenueFrontiers in Microbiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of GuelphUniversité Laval
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaNovalait
KeywordsSilageBiologyForageMicrobial inoculantLegumeAgronomyFood scienceRaw milkHayAnimal scienceInoculationHorticulture

Abstract

fetched live from OpenAlex

Introduction The microbiota of bulk tank raw milk is known to be closely related to that of microbial niches of the on-farm environment. Preserved forage types are partof this ecosystem and previous studies have shown variations in their microbial ecology. However, little is known of the microbiota of forage ration combinations and the transfer rates of associated species to milk. Methods We identified raw milk bacteria that may originate from forage rations encompassing either hay (H) or grass/legume silage uninoculated (GL) as the only forage type, or a combination of GL and corn silage uninoculated (GLC), or grass/legume and corn silage both inoculated (GLICI). Forage and milk samples collected in the fall and spring from 24 dairy farms were analyzed using 16S rRNA gene high-throughput sequencing following a treatment with propidium monoazide to account for viable cells. Results and discussion Three community types separating H, GL, and GLICI forage were identified. While the H community was co-dominated byEnterobacteriaceae,Microbacteriaceae,Beijerinckiaceae, andSphingomonadaceae, the GL and GLICI communities showed high proportions ofLeuconostocaceaeandAcetobacteraceae, respectively. Most of the GLC and GLICI rations were similar, suggesting that in the mixed forage rations involving grass/legume and corn silage, the addition of inoculant in one or both types of feed does not considerably change the microbiota. Raw milk samples were not grouped in the same way, as the GLC milk was phylogenetically different from that of GLICI across sampling periods. Raw milk communities, including the GLICI group for which cows were fed inoculated forage, were differentiated byEnterobacteriaceaeand other Proteobacteria, instead of by lactic acid bacteria. Of the 113 amplicon sequence variants (ASVs) shared between forage rations and corresponding raw milk, bacterial transfer rates were estimated at 18 to 31%. Silage-based forage rations, particularly those including corn, share more ASVs with raw milk produced on corresponding farms compared to that observed in the milk from cows fed hay. These results show the relevance of cow forage rations as sources of bacteria that contaminate milk and serve to advance our knowledge of on-farm raw milk contamination.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes2
Has abstractyes

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