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Record W4281763328 · doi:10.1016/j.heliyon.2022.e09696

Nutritional properties of wild and fattening mud crab (Scylla serrata) in the south-eastern district of Bangladesh

2022· article· en· W4281763328 on OpenAlexaff
Tajimul Islam, Debasish Saha, Shuva Bhowmik, Noordiana Nordin, Shabiha Islam, As-Ad Ujjaman Nur, Mohajira Begum

Bibliographic record

VenueHeliyon · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScylla serrataProximateBiologyAnimal scienceNutrientCrustaceanFisheryVeterinary medicineToxicologyFood scienceEcologyMedicine

Abstract

fetched live from OpenAlex

) is one of the most important crustacean species in Bangladesh due to its high economic value. Crab fattening is widely practiced in the country to meet export demand while the supply for domestic consumption is mainly dependent on wild sources. This work reported for the first time a comparative evaluation of the nutritional properties of wild and fattening mud crabs. For this purpose, the proximate compositions were determined in terms of total contents of protein, moisture, ash, lipid, and minerals. Female fattening mud crabs had the highest levels of protein (17.07 ± 1.52%) and moisture (76.95 ± 1.39%) content, while the highest percentages of ash (4.9 ± 1.03%) were detected in male fattening mud crabs. Male fattening mud crabs also contained high amounts of minerals, especially calcium (1199.71 ± 343.43 mg/100g) and iron (14.21 ± 1.28 mg/100g). Male and female wild crabs showed the highest levels of magnesium and phosphorus, respectively. Additionally, the calculated percentage of recommended nutrient intake (RNI) value revealed that mud crab contributes 4.4-59.99%, 6.6-53.98%, and 7.33-54.53% for infants, adults, pregnant and lactating women, respectively. The present study indicates that mud crab had a balanced nutritional composition that would be nutrient-rich excellent diet for consumers.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.207
Teacher spread0.187 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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