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Evaluation of the impact of functional foods on the course of Salmonella infection in piglets

2013· article· en· W266023390 on OpenAlexaff
Nadia Bergeron, Martin Lessard, Sylvain Quessy, Ann Letellier

Bibliographic record

VenueInternational Conference on the Epidemiology and Control of Biological, Chemical and Physical Hazards in Pigs and Pork · 2013
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSalmonellaCourse (navigation)Salmonella infectionFood scienceComputer scienceBiologyEngineeringBacteriaGenetics

Abstract

fetched live from OpenAlex

With the aim to improve growth of weaning piglets and to minimize incidence of intestinal diseases, the effect of a combination of functional foods able to stimulate the development of systemic and mucosal immune system and to modulate bacterial populations in the gut was evaluated. In this study, we assessed the impact of functional foods on the course of Salmonella infection in piglets. Piglets from different litters were weaned at 21 days of age and assigned to 1 of the 4 feed additives (8 litters per treatment) as follow: 1-control (CTRL), 2-antibiotic (ATB), 3-cocktail of functional foods (CFF), 4-bovine colostrum + cocktail of functional foods (COL-CFF).

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
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 head0.117
GPT teacher head0.389
Teacher spread0.272 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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