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Record W2530040325

Consumers and Manufacturers Perceptions about Nutrition Labeling in India-Proposed Labels for Indian Pickles

2005· article· en· W2530040325 on OpenAlexaboutno aff
Sonia Mini, Prerna Swati, Subadra Seshadri

Bibliographic record

VenueThe Indian Journal of Nutrition and Dietetics · 2005
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsNutrition facts labelProduct (mathematics)BusinessNutrition LabelingMarketingFood productsFood labelingNutrition informationEnvironmental healthMedicineFood science
DOInot available

Abstract

fetched live from OpenAlex

Food labels are an excellent avenue of communication. The modern package has assumed the responsibility of communicating the relevant information that a consumer needs to know about the product through the label besides performing its basic functions of containing and protecting. The WHO/FAO report on diet, nutrition and the prevention of chronic diseases suggested that nutrition labels are an important means of facilitating choice of and access to nutrient dense food. Cost benefit analysis suggest that the savings in the health care costs are relatively greater than the cost incurred by mandatory nutrition labeling3. In countries as USA, Australia, Canada and Brazil nutritional labeling is mandatory whereas in India it is voluntary. With the expanding local and export markets for processed foods, systematic studies on nutrition labeling may provide a much needed information base in decision making for both consumer and food industry.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.293
Teacher spread0.275 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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