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Record W4396618790 · doi:10.5376/ijh.2024.14.0010

Economics of Production and Marketing for French Bean in Kalikot District (Tilagupha Municipality), Nepal

2024· article· en· W4396618790 on OpenAlex
Susma Adhikari, Arati Chapai, Shova Shrestha, Nisha Bhandari, Prativa Acharya, Kiran Thapa, Sashi Kumar Keshari

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

VenueInternational Journal of Horticulture · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPineapple and bromelain studies
Canadian institutionsnot available
Fundersnot available
KeywordsProduction (economics)GeographyBusinessAgricultural economicsAgroforestryEconomicsBiology

Abstract

fetched live from OpenAlex

The research, conducted from February to July 2023 in Tilagupha municipality, Kalikot, Nepal, focused on French bean production and marketing.Sixty participants were surveyed using a stratified sampling technique.Primary data, gathered through household surveys, interviews, and field visits revealed insights into the agricultural landscape.Bean cultivation occurs once a year on small farms averaging 17.16 ropani, with 30.33% of land dedicated to beans.The average yield was 658.2 kg/ha, below the reported ADO Kalikot figure (1477 kg/ha).Production cost was Rs. 21,054.7 per ropani, with a return of Rs. 75,240 and a benefit-cost ratio of 1.20.Most producers (61.3%) were satisfied with bean prices.Challenges included diseases, pests, lack of irrigation, and limited marketing information, obtained mostly from neighbors (94.8%).The average retail price was Rs. 250 per kg, with a marketing margin of Rs. 78.34 per kg.Lack of market information was a significant issue in bean marketing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.190

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)0.0000.000

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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