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Record W2793473576 · doi:10.3390/agriculture8030044

Helping Agribusinesses—Small Millets Value Chain—To Grow in India

2018· article· en· W2793473576 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAgriculture · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsMcGill University
FundersGlobal Affairs CanadaInternational Development Research Centre
KeywordsAgribusinessStakeholderContext (archaeology)AgricultureBusinessGovernment (linguistics)Value (mathematics)Value chainMarketingChain (unit)Supply chainAgricultural scienceIndustrial organizationEconomicsGeographyManagementMathematicsBiology

Abstract

fetched live from OpenAlex

Small millets, a group of highly nutritious food, have taken a back seat in the Indian agriculture landscape in recent years, due to government policies and failings in the value chain. In this commentary, the unusual decline of small millets in comparison to its substitutes, and the repercussions thereof, were first presented as context. Thereafter, based on analysis of data from literature, survey, and stakeholder contributions, a cluster map for the Indian small millets value chain was designed, and its competitive state presented. This information was used to conceptualize an open innovation driven business model, and an ecosystem for the proposed model was discussed. This commentary provides the first cluster map analysis of small millets value chain in India, and a business model-based approach to stimulating its agribusinesses growth through a synthesis of stakeholders’ contributions and market data.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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