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Record W4414593115 · doi:10.26656/fr.2017.9(5).026

Sensory evaluation and proximate composition of the mangrove Stone Crab (Myomenippe hardwickii) (Gray, 1831) claw meat

2025· article· en· W4414593115 on OpenAlexfundno aff
Lirong Yu Abit, Natrah Fatin Mohd Ikhsan, Kamil Latif

Bibliographic record

VenueFood Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsClawProximateSensory analysisScylla serrataComposition (language)

Abstract

fetched live from OpenAlex

Crab claw meat is a popular seafood delicacy valued for its rich nutritional content and appealing sensory qualities. The mangrove Stone Crab (Myomenippe hardwickii) claw meat was compared to four other commercially available fresh crab claw meats (Portunus pelagicus, P. sanguinolentus, Scylla tranquebarica and Charybdis natator) in terms of proximate composition and sensory evaluation. In terms of proximate composition M. hardwickii claw meat was found to comprise 79.1% moisture, 1.98% ash, 22.03% protein and 2.1% lipid; these values were not significantly different from values obtained from the crab claw meat of the other crab species used in this study. For sensory evaluation, it was found that M. hardwickii claw meat compared favourably to other crab claw meats with high scores in terms of texture, taste, scent and overall acceptance. The findings suggest that M. hardwickii claw meat is nutritionally comparable and sensory appealing, making it a viable seafood alternative.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.080
GPT teacher head0.350
Teacher spread0.270 · 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
Published2025
Admission routes1
Has abstractyes

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