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Record W2613065664 · doi:10.3168/jds.2016-12239

Understanding the gut microbiome of dairy calves: Opportunities to improve early-life gut health

2017· article· en· W2613065664 on OpenAlexafffund
Nilusha Malmuthuge, Le Luo Guan

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsGut floraMicrobiomeGut microbiomeBiologyGut microfloraPsychological interventionImmune systemImmunologyMedicineBioinformaticsBacteria

Abstract

fetched live from OpenAlex

Early gut microbiota plays a vital role in the long-term health of the host. However, understanding of these microbiota is very limited in livestock species, especially in dairy calves. Neonatal calves are highly susceptible to enteric infections, one of the major causes of calf death, so approaches to improving gut health and overall calf health are needed. An increasing number of studies are exploring the microbial composition of the gut, the mucosal immune system, and early dietary interventions to improve the health of dairy calves, revealing possibilities for effectively reducing the susceptibility of calves to enteric infections while promoting growth. Still, comprehensive understanding of the effect of dietary interventions on gut microbiota-one of the key aspects of gut health-is lacking. Such knowledge may provide in-depth understanding of the mechanisms behind functional changes in response to dietary interventions. Understanding of host-microbial interactions with dietary interventions and the role of the gut microbiota during pathogenesis at the site of infection in early life is vital for designing effective tools and techniques to improve calf gut 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 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)0.003
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.202
GPT teacher head0.379
Teacher spread0.177 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations164
Published2017
Admission routes2
Has abstractyes

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