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Record W2962251244 · doi:10.1111/1471-0307.12630

Microbial, physico‐chemical and sensory characteristics of mango juice‐enriched probiotic dairy drinks

2019· article· en· W2962251244 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

VenueInternational Journal of Dairy Technology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFood scienceProbioticLactobacillus acidophilusFermentationChemistryPrebioticSensory analysisFruit juiceBiologyBacteria

Abstract

fetched live from OpenAlex

This study aimed to determine whether mango juice can improve the viability of probiotics in a fermented dairy‐based beverage whilst maintaining its quality characteristics. Formulations containing Lactobacillus acidophilus La‐5 culture, whole cow's milk and varying concentrations of mango juice (0%, 10%, 20%, 30% and 40% (w/w)) were produced and stored for five weeks at 4 °C. Results showed that probiotic viability was enhanced with the addition of 10% mango juice. Additionally, this formulation improved probiotics tolerance when exposed to in vitro gastrointestinal digestion. According to the sensory analysis, beverage sensory scores improved as levels of mango juice increased from 20% to 40%.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.223
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.008
GPT teacher head0.216
Teacher spread0.209 · 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