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Record W2910877143 · doi:10.22092/ijfs.2018.117675

Effects of different cooking methods on minerals, vitamins and nutritional quality indices of grass carp (Ctenopharyngodon idella)

2019· article· en· W2910877143 on OpenAlexfundno aff
Sara Golgolipour, Ainaz Khodanazary, Kamal Ghanemi

Bibliographic record

VenueAquaDocs (United Nations Educational, Scientific and Cultural Organization) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersOrganization for Security and Co-operation in EuropeInstituto Colombiano de Bienestar FamiliarSveriges RegeringInter-American Development BankJapan International Cooperation AgencyEuropean CommissionCanadian Institute for Theoretical Astrophysics
KeywordsGrass carpFood scienceChemistryProximateVitaminCooking methodsFatty acidVitamin CFish <Actinopterygii>BiologyFisheryBiochemistry

Abstract

fetched live from OpenAlex

This study aimed to evaluate the nutritional value (proximate composition, fatty acid profiles, vitamins and minerals) contents and also nutritional quality indices (NQI)) of grass carp (Ctenopharyngodon idella) prepared according to common consumer techniques: raw, poached, steamed, microwaved, pan-fried and deep-fried (in olive oil). In comparison to raw fish fillets, when grass carp was cooked there was an increase in protein, lipid and ash contents. Cooking methods had no significant effect on total n-3 fatty acids except for frying fillets. Lowest and highest content of n-3 was shown in deep-fried and pan-fried samples, respectively. Total n-6 fatty acid of cooked samples increased in comparison to raw samples. Na, K, Mg, P and Zn contents of boiled fish fillets significantly decreased. None of cooking methods had a significant effect a vitamin D. However, vitamin A, B_1 and B_3 contents of cooked fish significantly decreased.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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