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Record W4407557251 · doi:10.5376/bm.2024.15.0032

Case Study of Post-Harvest Processing and Value Addition in Fresh-Eating Sweet Potato

2024· article· en· W4407557251 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.

venuePublished in a venue whose home country is Canada.
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

VenueBioscience Methods · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Food scienceAgricultural scienceBusinessHorticultureBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

This study explores the impact of post-harvest processing and value-added methods for fresh sweet potatoes on farmers' income and market demand. The research finds that value-added processing of sweet potatoes, such as producing sweet potato flour, chips, and puree, helps increase farmers' income, reduce post-harvest losses, and extend product shelf life. Additio nally, farmer cooperatives and agricultural groups play a significant role in promoting value-added activities by providing training, technical support, and market access. The study recommends further promotion of value-added processing technologies, improved credit access for smallholders, and the establishment of better market linkages through government and non-governmental organizations to enhance the market value of sweet potato products and boost farmers' economic returns.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.090
GPT teacher head0.398
Teacher spread0.307 · 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