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Record W2130732521 · doi:10.5267/j.uscm.2015.4.004

Modeling cold supply chain environment of organized farm products retailing in India

2015· article· en· W2130732521 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

VenueUncertain Supply Chain Management · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainBusinessCold chainChain (unit)Industrial organizationMarketingEnvironmental economicsFood scienceEconomics

Abstract

fetched live from OpenAlex

Organized retailing (OZR) is one of the most promising industries in India. The OZR sector of India is now among the top five fastest growing markets of the world. High population, globalization, and the increasing income of middle class etc. are a number of factors that make Indian market more challenging and competitive. In today's globalized world, effective supply chain performance (SCP) is highly important to ensure the productivity in the whole supply chain (SC). In this paper, we investigated the factors that affect cold supply chain performance (CSCP) of organized farm products retailing (OFPR), developed and validated a model. This research will be helpful to formulate better strategies for this sector to a greater extent; this model will also be useful and applicable for other developing Asian countries because of enormous similarities in their market tendencies.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.210
Teacher spread0.183 · 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