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Record W3201451007

NUTRITIONAL COMPOSITION OF VARIOUS PACKAGED DRIED CHIPS AVAILABLE IN PAKISTANI MARKET

2020· article· en· W3201451007 on OpenAlexvenueno aff
Abdul Momin Rizwan Ahmed, Umar Farooq, Maria Anwar

Bibliographic record

VenueAdvanced Food and Nutritional Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceCalorieComposition (language)Dietary fiberChemistryMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

Background: Dried chips are among the most commonly consumed snacks in Pakistan not only by children but also by adults. Despite the fact that they are consumed in large amounts, they pose several health hazards. The major objective of the present research was to determine the nutritional composition of various packaged dried chips available in the local market.Methodology: Eight most sought after brands of dried chips were chosen and analyzed with regards to their nutritional composition.Results: The results showed that one serving of dried chips (30 grams) provided 149.62 ± 19.10 calories. Moreover, one serving of dried chips contributed to 14.99 ± 4.14 grams of carbohydrates while 2.05 ± 3.63 grams of sugars. Fats provided by one serving of chips were calculated to be as 7.40 ± 3.63 grams. An alarming figure was that only one serving of dried chips contributed to 3.44 ± 1.94 grams of saturated fats. Moreover, a serving of chips provided 1.99 ± 0.82 grams of protein while only 0.82 ± 0.57 grams of fiber. Likewise, a high amount of sodium per serving was seen to be contributed by a serving of chips, which was 205.60 ± 37.43 mg.Conclusions: The study demonstrated that the dried chips were not high in calories but also contributed to lots of carbohydrates and saturated fats. Moreover, the fiber content provided by chips was almost negligible.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.481

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 studies0.0000.000
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 head0.025
GPT teacher head0.269
Teacher spread0.245 · 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
Published2020
Admission routes1
Has abstractyes

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