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Record W3024178629 · doi:10.1111/1541-4337.12564

Plant carotenoids evolution during cultivation, postharvest storage, and food processing: A review

2020· review· en· W3024178629 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 · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsMcGill University
FundersThailand Research Fund
KeywordsPostharvestCarotenoidFood processingFood scienceFood preservationFood storageEnvironmental scienceHorticultureBiology

Abstract

fetched live from OpenAlex

Carotenoids in nature are predominantly C40 hydrocarbons that may contain oxygenated functional groups. Although they are well-recognized to exhibit key human health benefits, they cannot be synthesized in the human body and must be obtained from the diet. Fruit and vegetables are the primary dietary sources of carotenoids because plants automatically synthesize these compounds to protect cells from oxidative damage that may occur upon photosynthesis due to light. Biosynthesis and accumulation of carotenoids in plants begin during cultivation through postharvest storage. However, these compounds naturally degrade upon plant senescence and also during food processing (e.g., blanching, pasteurization, and drying). In this article, evolution of carotenoids during cultivation, postharvest storage, and food processing is comprehensively reviewed. Appropriate conditions and methods to cultivate, store, and process fruit and vegetables to help retard carotenoid degradation and enhance carotenoid biosynthesis are also reviewed and identified.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.069
GPT teacher head0.291
Teacher spread0.222 · 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