MétaCan
Menu
Back to cohort
Record W30047633

GENETIC DIVERSITY IN POPULATIONS OF SEPIELLA JAPONICA BASED ON THE MITOCHONDRIAL DNA SEQUENCE ANALYSIS

2005· article· en· W30047633 on OpenAlexvenueno aff
Xiaodong Zheng, Qi Li, Zhaoping Wang, Ruihai Yu, Chuanyuan Tian, Rucai Wang

Bibliographic record

VenueHealth law in Canada · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsHaplotypeBiologyMitochondrial DNAGenetic diversityCephalopodCuttlefishNucleotide diversityChinaZoologyJaponicaGeneticsEvolutionary biologyGeographyGeneFisheryGenotypePopulationDemographyArchaeologyBotany
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT: Part of the 16S rRNA gene was amplified with PCR and sequenced for 57 individuals from5 populations of common Chinese cuttlefish Sepiella japonica : three from the South China Sea, onefrom East China Sea and one from Nagasaki water (Japan). The result showed that a total of 5 nucleotidepositions were found to have insertions/deletions among these individuals, and 13 positions wereexamined to be variable in all the sequences, which ranged from 494 to 509 base pairs. All of theindividuals were grouped into 7 haplotypes (h1–h7). The individuals from Nagasaki belonged to h1and the h3 haplotype was found only in the coastal waters of China. A ↔G transition in Nucleotide 255was suggested to be taken as a kind of genetic marker to identify the populations distributed in East-South China Sea and the Nagasaki water of Japan. INTRODUCTION Cephalopods are a potentially valuable proteinresource and highly promising animals formariculture because of such characters as highnutritional qualities, short life span and extremelyrapid growth (Nesis, 1987). China is one of themajor countries of cephalopod fisheries. It boastsrich species resources: 101 species belonging to 6orders, 21 families and 45 genera (Huang, 1994)have been discovered.

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 score0.044
Threshold uncertainty score0.254

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 head0.051
GPT teacher head0.260
Teacher spread0.209 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueHealth law in CanadaSame topicCephalopods and Marine BiologyFrench-language works237,207