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Assessment of Operational Efficiency Among Wholesalers and Retailers of Vegetables in Igalaland of Kogi State, Nigeria

2013· article· en· W1678998030 on OpenAlexvenueno aff
Elijah E. Ogbadu

Bibliographic record

VenueCanadian social science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOperational efficiencyCommodityBusinessConsumption (sociology)Distribution (mathematics)Production (economics)Margin (machine learning)Agricultural economicsState (computer science)Value (mathematics)Environmental economicsAgricultural scienceMarketingOperations managementEconomicsComputer scienceFinanceMicroeconomicsStatisticsMathematicsEnvironmental science

Abstract

fetched live from OpenAlex

Assessment of operational efficiency among wholesales and retailers is one of the many measures of market performance. The costs of operations are heightening by cost of transportation on the bad roads. Vegetables are perishable products and needs to be distributed as quickly as possible. The objective of the study is to assess the operational efficiency of vegetable distribution in Igala land of Kogi State. The wholesalers and retailers of vegetables from each of the three main markets of the commodity were randomly selected for the study. Analysis of data collected by use of questionnaire was accomplished through employing analytical models such as relative operational efficiency, operational efficiency coefficient, marketing margin and multiple regressions. Results showed that operational efficiency varied among the two classes of middlemen in the three markets and was significantly affected by experience, speed of distribution and total value of sales. Better results can be achieved if production and consumption are stimulated through efficient distribution system.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.210
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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