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Record W3156779529 · doi:10.3390/fermentation7020063

Enhancing Micronutrients Bioavailability through Fermentation of Plant-Based Foods: A Concise Review

2021· review· en· W3156779529 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

VenueFermentation · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsUniversity of Manitoba
FundersScience and Engineering Research BoardDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsBioavailabilityMicronutrientNutrientFood scienceEssential nutrientBiofortificationChemistryFermentationManganeseBiotechnologyBiology

Abstract

fetched live from OpenAlex

Plant-based foods are rich sources of vitamins and essential micronutrients. For the proper functioning of the human body and their crucial role, trace minerals (iron, zinc, magnesium, manganese, etc.) are required in appropriate amounts. Cereals and pulses are the chief sources of these trace minerals. Despite these minerals, adequate consumption of plant foods cannot fulfill the human body’s total nutrient requirement. Plant foods also contain ample amounts of anti-nutritional factors such as phytate, tannins, phenols, oxalates, etc. These factors can compromise the bioavailability of several essential micronutrients in plant foods. However, literature reports show that fermentation and related processing methods can improve nutrient and mineral bioavailability of plant foods. In this review, studies related to fermentation methods that can be used to improve micronutrient bioavailability in plant foods are discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.344
Teacher spread0.265 · 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