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Electrophoresis and Blotting of <scp>DNA</scp>

2013· other· en· W1498549766 on OpenAlexaff
Katharine Sedivy‐Haley, Sandeep Tamber, Robert E. W. Hancock

Bibliographic record

VenueEncyclopedia of Life Sciences · 2013
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Genetic and Mutation Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDNABlotGel electrophoresisSouthern blotDNA sequencingMolecular biologyPolymerase chain reactionComputational biologyBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Deoxyribonucleic acid (DNA) electrophoresis and blotting are techniques commonly used to visualise DNA. Both techniques use simple, inexpensive protocols that rely on fundamental properties of DNA, and these protocols have changed little since they were introduced. Electrophoresis relies on the negative electrical charge of DNA to draw these molecules through a gel, separating DNA molecules on the basis of size. Southern blotting makes use of sequence complementarity to identify specific DNA fragments on the basis of their sequences. Despite the introduction of more powerful methods such as polymerase chain reaction and DNA sequencing, electrophoresis and blotting techniques are still widely used to identify DNA fragments of interest, including the identification of unusual structures within DNA and the analysis of larger fragments which are not easily analysed by other methods. Key Concepts: DNA electrophoresis and blotting are fundamental methods of molecular biology. Basic physical and chemical properties of DNA are used to analyse unknown sequences. Gel electrophoresis uses an electrical current to separate DNA by size. Southern blotting locates a specific DNA sequence on a gel using a probe sequence that is its chemical match. Minor variations on these procedures can improve results for specific experiments. These techniques are still used to analyse unknown DNA molecules and identify DNA fragments of interest. The principles behind electrophoresis and blotting can also be seen in newer methods.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.259
Threshold uncertainty score0.302

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2013
Admission routes1
Has abstractyes

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