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Record W3042765278 · doi:10.1139/gen-2019-0163

Genetic diversity and phylogenetic relationships of threadfin breams (<i>Nemipterus</i> spp.) from the Red Sea and eastern Mediterranean Sea

2020· article· en· W3042765278 on OpenAlexvenueno aff
Joel Ogwang, Michel Bariche, Arthur R. Bos

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyHaplotypeMediterranean seaPhylogenetic treeGenetic diversityZoologyPopulationMediterranean climateFisheryEcologyGeneticsGenotypeDemographyGene

Abstract

fetched live from OpenAlex

The present work utilized partial sequences of cytochrome c oxidase subunit I (COI) to study Red Sea populations of threadfin breams (Nemipteridae), and compare their genetic diversity to that of Mediterranean Sea (Nemipterus randalli only) and Indo-Pacific populations. A Maximum Likelihood tree separated four fish species — N. randalli, N. japonicus, N. bipunctatus, and N. zysron — into four clades. Haplotype analyses revealed a strong case of the founder effect for the Lessepsian migrant N. randalli: Three haplotypes represented all sampled geographical ranges in the Mediterranean Sea and only one haplotype was shared with a Red Sea individual, presenting evidence that the colonizing population was founded by a small number of migrants. The Red Sea population of N. japonicus shared haplotypes with Persian Gulf and Indian Ocean populations, but South China Sea populations remained fully isolated. The haplotype networks of N. randalli and N. bipunctatus also revealed haplotype sharing between Red Sea and Indian Ocean populations. For N. zysron, one haplotype was shared between Indonesia and the Persian Gulf. We discuss the impact of continued usage of public database sequences of initially misidentified organisms and provide recommendations for avoiding distribution of sequences with incorrect scientific names.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.372

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)0.0000.000

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 head0.050
GPT teacher head0.229
Teacher spread0.179 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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