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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&#13;\nprofiles, vitamins and minerals) contents and also nutritional quality indices (NQI)) of&#13;\ngrass carp (Ctenopharyngodon idella) prepared according to common consumer&#13;\ntechniques: raw, poached, steamed, microwaved, pan-fried and deep-fried (in olive oil).&#13;\nIn comparison to raw fish fillets, when grass carp was cooked there was an increase in&#13;\nprotein, lipid and ash contents. Cooking methods had no significant effect on total n-3&#13;\nfatty acids except for frying fillets. Lowest and highest content of n-3 was shown in&#13;\ndeep-fried and pan-fried samples, respectively. Total n-6 fatty acid of cooked samples&#13;\nincreased in comparison to raw samples. Na, K, Mg, P and Zn contents of boiled fish&#13;\nfillets significantly decreased. None of cooking methods had a significant effect a&#13;\nvitamin D. However, vitamin A, B_1 and B_3 contents of cooked fish significantly&#13;\ndecreased.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
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 head0.016
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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