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Record W2898364736 · doi:10.1002/9781119289470.ch9

Hexanal Compositions for Enhancing Shelf‐life and Quality in Papaya

2018· other· en· W2898364736 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicPapaya Research and Applications
Canadian institutionsnot available
FundersGlobal Affairs CanadaInternational Development Research Centre
KeywordsPostharvestPreharvestShelf lifeFood scienceRiboflavinHexanalHorticultureBiologyChemistry

Abstract

fetched live from OpenAlex

Papaya is popular with consumers due to its nutritional value and flavor characteristics. This chapter describes the importance of papaya as a nutritious fruit and the methods used to extend the postharvest life of this commodity using postharvest treatments, including formulations, based on the natural plant product called hexanal. Papaya is rapidly becoming an important fruit crop both as a fresh fruit and in the processed form. Papaya provides a cheap source of vitamins and minerals in the daily diet of people. The contribution of papaya to food nutritional security should not be undervalued as papaya ranks at the top among the fruits for vitamin C, vitamin A, riboflavin, folate, calcium, thiamine, iron, niacin, potassium, and fiber. Papaya has a limited postharvest life due to its perishable nature. Application of suitable preharvest and postharvest techniques is important for increasing the biological stability and extending the shelf-life of this commodity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.998

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)0.0030.000

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 head0.063
GPT teacher head0.417
Teacher spread0.354 · 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

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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