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Record W2148794153 · doi:10.1186/1471-2156-7-27

CapsID: a web-based tool for developing parsimonious sets of CAPS molecular markers for genotyping

2006· article· en· W2148794153 on OpenAlexafffund
Jonathan Taylor, Nicholas J. Provart

Bibliographic record

VenueBMC Genetics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversities Space Research Association
KeywordsGenotypingCapsidBiologyGeneticsComputational biologyRestriction fragment length polymorphismRestriction enzymeSingle-nucleotide polymorphismEndonucleaseSNP genotypingGenotypeGene

Abstract

fetched live from OpenAlex

BACKGROUND: Genotyping may be carried out by a number of different methods including direct sequencing and polymorphism analysis. For a number of reasons, PCR-based polymorphism analysis may be desirable, owing to the fact that only small amounts of genetic material are required, and that the costs are low. One popular and cheap method for detecting polymorphisms is by using cleaved amplified polymorphic sequence, or CAPS, molecular markers. These are also known as PCR-RFLP markers. RESULTS: We have developed a program, called CapsID, that identifies snip-SNPs (single nucleotide polymorphisms that alter restriction endonuclease cut sites) within a set or sets of reference sequences, designs PCR primers around these, and then suggests the most parsimonious combination of markers for genotyping any individual who is not a member of the reference set. The output page includes biologist-friendly features, such as images of virtual gels to assist in genotyping efforts. CapsID is freely available at http://bbc.botany.utoronto.ca/capsid. CONCLUSION: CapsID is a tool that can rapidly provide minimal sets of CAPS markers for molecular identification purposes for any biologist working in genetics, community genetics, plant and animal breeding, forensics and other fields.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.818

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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 designBench or experimental
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

Citations12
Published2006
Admission routes2
Has abstractyes

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