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Record W4390742009 · doi:10.3168/jds.2023-24002

Exploring the effects of transport duration on the fecal microbial communities of surplus dairy calves

2024· article· en· W4390742009 on OpenAlexaffabout
Poonam Gopika Vinayamohan, Jelmer W. Poelstra, Ting‐Yu Cheng, H.M. Goetz, D.L. Renaud, Diego E. Gómez, Gregory Habing

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFusobacteriaFirmicutesFecesBacteroidetesBiologyActinobacteriaProteobacteriaMicrobiomeBifidobacteriumBacteroidesVeterinary medicineLactobacillusEcologyFood science16S ribosomal RNABacteriaMedicine

Abstract

fetched live from OpenAlex

Transportation significantly affects the health and welfare of surplus dairy calves, largely due to the various stressors and pathogen exposures encountered during the process. Concurrently, an animal's microbiome is known to correlate with its health status, with stress-induced alterations in the microbiota potentially precipitating various diseases. This study aimed to compare the effects of transportation durations of 6, 12, or 16 h on the fecal microbiota in young surplus dairy calves. We used a randomized controlled design in which surplus dairy calves aged 1 to 19 d from 5 commercial dairy farms in Ontario were allocated into 1 of 3 transportation groups (6, 12, and 16 h of continuous transportation). Health assessments were conducted before, immediately after, and for 2 wk following transportation. Fecal samples were collected before, immediately after, and at 24 and 72 h after transportation and subjected to 16S rRNA sequencing. Alpha diversity metrics showed no significant differences between the 3 transportation groups at any of the sampling time points. Although β diversity metrics revealed no clustering by transportation groups, they indicated significant differences across sampling time points within each group. The overall analysis revealed a total of 22 phyla and 353 genera, with Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria being the most abundant phyla. Bacteroides, Escherichia/Shigella, Lactobacillus, Collinsella, and Bifidobacterium were the most abundant genera. The reduction in Fusobacteria abundance before and after transport was significantly larger in the 16-h transportation group when compared with the 6-h transportation group. We also identified several genus-level and amplicon sequence variation-level taxa that displayed significant differences in their abundances across various transportation groups, observed at all sampling time points investigated. This research identifies microbiota changes due to varying transportation durations in surplus dairy calves, providing a broad understanding of the microbial shifts in surplus dairy calves after transportation across varying durations. Although these variations may not directly correlate with overall calf health or indicate dysbiosis, these results emphasize the importance of further investigating transportation practices to enhance calf health and well-being. Further studies are warranted to elucidate the relationship between microbiota and calf health.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.274
Teacher spread0.239 · 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
Published2024
Admission routes2
Has abstractyes

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