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Record W4205938788 · doi:10.1080/87559129.2021.2013498

Beneficial Effects of Bioactive Compounds Obtained from Agro-Industrial By-Products on Obesity and Metabolic Syndrome Components

2022· article· en· W4205938788 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

VenueFood Reviews International · 2022
Typearticle
Languageen
FieldNursing
TopicPomegranate: compositions and health benefits
Canadian institutionsUniversity of Toronto
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsNutraceuticalObesityRaw materialBiotechnologyMetabolic syndromeFood scienceMedicineBiologyEndocrinology

Abstract

fetched live from OpenAlex

The generation of agro-industrial by-products is an economic and environmental problem. However, these raw materials could be a suitable source for obtaining bioactive compounds for technological or nutritional purposes. On the other hand, obesity and metabolic syndrome prevalence are in continuous growth. The classical approach of hypocaloric diet and exercise has shown little long-term adherence. Thus, there is an unending search for new strategies to prevent and treat obesity and related metabolic alterations. In that sense, the revalorization of agro-industrial by-products for functional foods and nutraceutical development has gained relevance. Pomegranate, onion, and grape by-products, among others, have been described as promising raw materials for bioactive compounds obtention. Nevertheless, scientific evidence on the effects of specific sources and bioactive compounds on obesity models and clinical trials is needed. This article aims to show available data from studies on the effect of bioactive compounds obtained from agro-industrial by-products on obesity and metabolic syndrome components.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.281
Teacher spread0.241 · 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