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Record W4322492849 · doi:10.1080/07373937.2023.2182316

A simple solar crop drying and pasteurizing system appropriate for smallholder and subsistence farmers in tropical and subtropical regions

2023· article· en· W4322492849 on OpenAlexaff
Andrew G. Watson, Arun S. Mujumdar, Bhaskar N. Thorat, Shivanand S. Shirkole, Nikita S. Bhatkar

Bibliographic record

VenueDrying Technology · 2023
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsSubsistence agricultureAgricultureBusinessTropicsEnvironmental scienceAgricultural engineeringAgricultural scienceNatural resource economicsAgroforestryAgricultural economicsEngineeringEconomicsBiologyEcology

Abstract

fetched live from OpenAlex

Open sun drying is the predominant process used for food preservation by smallholder and subsistence farmers (SHSFs), and their families, globally constituting about 2 billion people on about 475 million farms. The major problems with this process are the slow, unreliable speed of completion, rain-caused loss of vital food sources, risks from mycotoxin accumulation, environmental contamination, and loss of nutritional quality. SHSFs represent the poorest and least educated human demographic, whose survival is largely ignored by Governments, untouched by international aid programs, and with limited access to basic utilities. Solar drying systems can greatly improve the preservation of safe and nutritious food, but current systems deployed put this technology beyond the reach of the demographic which needs it most. Here, our objective was to develop the technology for the simplest low-cost drying system, which would minimize all risk factors, particularly eliminate mycotoxin accumulation, and which could be practically deployed by SHSFs.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.045
GPT teacher head0.309
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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