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Record W2157058754 · doi:10.5376/ijms.2015.05.0055

Sequence Analysis and Molecular Phylogeny of 16S rRNA Gene Fragments in Four Species of the Penaeid Shrimps from the Sudanese Red Sea

2015· article· en· W2157058754 on OpenAlexvenueno aff
Mohd Yusof Ibrahim

Bibliographic record

VenueInternational Journal of Marine Science · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
Fundersnot available
Keywords16S ribosomal RNABiologyPhylogeneticsGene sequenceGeneZoologyMolecular phylogeneticsSequence (biology)Ribosomal RNAEvolutionary biologyGenetics

Abstract

fetched live from OpenAlex

Penaeid shrimps are of biological and economic importance and are highly in demand for human consumption. Four species of the penaeid decapod crustaceans, Finneropenaeus indicus , Penaeus monodon , P. semisulcatus and Metapenaeus monoceros were studied. Haplotype and nucleotide diversity were combined to assess the phylogenetic relationships of the penaeid shrimp species and populations of the Finneropenaeus indicus . Shrimp specimens were collected at different locations of Baaboud and Alkhairat aquafarms and from the wild. Phylogenetic relationships among the penaeid shrimp species and genetic diversity of F. indicus populations were assessed using partial mtDNA 16S rRNA gene (480 bp). Genetic distances among the species were done. The Genetic differentiation between F. indicus population (Baaboud –Alkhairat; Baaboud-Wild; Alkhairat- Wild) was found 0.10957, 0.12459 and 0.14817 respectively. No clear indication of differentiation between 16S rRNA tree branches of the populations of Alkhairat and Baaboud from the wild population, which may be attributed to the common collection sites of brood stocks and/or post larvae (PL’s) besides the absence of hydrological and physical barriers. M. monoceros formed a distant sister taxon to all other Penaeus species.

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 score0.324
Threshold uncertainty score0.172

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 science0.0010.001
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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