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Record W2780957046 · doi:10.5376/ijh.2017.07.0030

Post Harvest Processing of Moringa and Socio-Economic Appraisal of Moringa Orchards in Tamil Nadu

2017· article· en· W2780957046 on OpenAlexvenueno aff
C. Sekhar, Nandini Venkatesan, A. Vidhyavathi, M Murugananthi

Bibliographic record

VenueInternational Journal of Horticulture · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMoringa oleifera research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsMoringaTamilAgroforestryEnvironmental scienceBiologyArtFood science

Abstract

fetched live from OpenAlex

The word Moringa is Magic to Many of the consumers both in India and many other countries. Because of the nutritional and medicinal importance of Moringa, the demand for Moringa and its value added products are increasing which in turn permits enhanced area under Moringa from the supply side and hence a study has been taken up in the Western and Southern Part of Tamil Nadu to analyze the reasons for taking up Moringa plantations in a large scale and their economics. This paper has identified few factors which are influencing the cultivation of Moringa and the factors governing the profitability of Moringa. The Economic appraisal tools have revealed that the Moringa cultivation is profitable and hence the detailed analysis of costs and their return is presented and discussed. Besides, the nutritional and medicinal importance coupled with the steps involved in post harvest processing are also discussed for the benefit of Processors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.318
Teacher spread0.298 · 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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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