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Record W2078196872 · doi:10.1071/mf07215

Looking through glassfish: marine genetic structure in an estuarine species

2008· article· en· W2078196872 on OpenAlexaff
Courtenay E. Mills, Wade L. Hadwen, Jane Hughes

Bibliographic record

VenueMarine and Freshwater Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsNorth Island College
Fundersnot available
KeywordsPanmixiaBiological dispersalBiologyGenetic structurePhylogeographyIsolation by distanceEstuaryEcologyGenetic diversityPopulation geneticsPopulationEvolutionary biologyGenetic variationPhylogeneticsGeneticsGene

Abstract

fetched live from OpenAlex

Through the use of mitochondrial DNA (ATP8 gene), the prediction of intermediate genetic structuring was investigated in two species of estuarine glassfish (Ambassis marianus and Ambassis jacksoniensis) (Perciformes : Ambassidae) to determine the possibility of a generalised ‘estuarine’ genetic structure. Individuals were collected from estuaries in eastern Australia between Tin Can Bay (Queensland) in the north and Kempsey (New South Wales) in the south. Analysis of the haplotype frequencies found in this region suggested panmictic populations with star-like phylogenies with extremely high levels of genetic diversity, but with no correlation between geographic distance and genetic distance. Non-significant FST and FST suggested extensive dispersal among estuaries. However, Tajima’s D and Fu’s FS values suggest ‘mutation–genetic drift equilibrium’ has not been reached, and that population expansions occurring 262 000 (A. marianus) and 300 000 (A. jacksoniensis) years ago may obscure any phylogeographic structuring or isolation by distance. The finding of panmixia was contrary to the prediction of genetic structuring intermediate between that of marine fish (shallowly structured) and freshwater fish (highly structured), suggesting high dispersal capabilities in these 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 score0.109
Threshold uncertainty score0.969

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 science0.0000.001
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 head0.053
GPT teacher head0.300
Teacher spread0.247 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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