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Simple identification of mitochondrial lineages in contact zones based on lineage‐selective primers

2007· article· en· W2155393712 on OpenAlexaff
Johan Lindell, Robert W. Murphy

Bibliographic record

VenueMolecular Ecology Resources · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
Fundersnot available
KeywordsBiologyLineage (genetic)Mitochondrial DNAEvolutionary biologyHaplotypeIntraspecific competitionGeneticsMonophylyPolymerase chain reactionPhylogeneticsZoologyGeneAlleleClade

Abstract

fetched live from OpenAlex

A variety of research projects focus on genetic variation among and within maternal lineages as encompassed by mitochondrial DNA (mtDNA). While mtDNA often differs substantially between species, large differences may also be found within species. The evaluation of such divergent lineages, for example in intraspecific contact zones (hybrid zones), commonly involves sequencing numerous individuals. Large-scale sequencing is both expensive and labour-intensive. Based on sequences from 15 individuals, we devised a simple and quick polymerase chain reaction assay for identification of divergent mtDNA lineages in a secondary contact zone of the side-blotched lizard (Uta stansburiana). The application uses lineage-selective primers to amplify a lineage-diagnostic product, and is based on each group of mtDNA haplotypes being a monophyletic assemblage of haplotypes sharing the same maternal ancestry, deeply divergent from the other group. The assay was tested on a larger sample (n = 147) of specimens from the contact zone, confirming its usefulness in quick and reliable identification of mtDNA lineages. This approach can be modified for other species, provided diagnostic lineage variation is available, and may also be performed in simple laboratory settings while conducting fieldwork.

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 score0.378
Threshold uncertainty score0.461

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 head0.005
GPT teacher head0.232
Teacher spread0.228 · 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
Published2007
Admission routes1
Has abstractyes

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