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Record W2969319758 · doi:10.1111/1541-4337.12477

Recovery of High Value‐Added Nutrients from Fruit and Vegetable Industrial Wastewater

2019· article· en· W2969319758 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

VenueComprehensive Reviews in Food Science and Food Safety · 2019
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural Affairs
FundersBiotechnology and Biological Sciences Research Council
KeywordsReuseBusinessWastewaterFood industryEnvironmental scienceBiotechnologyWaste managementPulp and paper industryFood scienceChemistryEngineeringEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

The industrial processing water of fruit and vegetables has raised serious environmental concerns due to the presence of many important bioactive compounds being disposed in the wastewater. Bioactive compounds have great potential for the food industry to optimize their process and to recover these compounds in order to develop value-added products and to reduce environmental impacts. However, to achieve this goal, some challenges need to be addressed such as safety assurance, technology request, product regulations, cost effectiveness, and customer factors. Therefore, this review aims to summarize the recent advances of bioactive compounds recovery and the current challenges in wastewater from fruit and vegetable processing industry, including fruit and beverage, soybean by-products, starch and edible oil industry. Moreover, future direction for novel and green technology of bioactive compounds recovery are discussed, and a prospect of bioactive compounds reuse and sustainable development is proposed.

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)0.001
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 score0.218
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.062
GPT teacher head0.278
Teacher spread0.215 · 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