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Record W4240101819 · doi:10.1002/047001153x.g202310

Fingerprint mapping

2005· other· en· W4240101819 on OpenAlexaff
Jacqueline E. Schein, Martin Krzywinski

Bibliographic record

VenueEncyclopedia of Genetics, Genomics, Proteomics and Bioinformatics · 2005
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsFingerprint (computing)GenomeComputational biologyBiologyGenomicsclone (Java method)Restriction enzymeGeneticsCaenorhabditis elegansComputer scienceArtificial intelligenceGene

Abstract

fetched live from OpenAlex

Abstract Restriction fingerprint maps have played a significant role in genomics research over the past 35 years. Representing the size and order of restriction endonuclease fragments along the genome, these maps provided a physical framework on which to locate known features and functions in genomes of early scientific interest. Over the last 20 years, large‐insert, clone‐based fingerprint maps have been critical components in genome mapping and sequencing projects, including those for Caenorhabditis elegans , Saccharomyces cervisiae , Mus musculus , and Homo sapiens . In this review, we provide a summary of the evolution of fingerprint mapping, and an overview of the current methodologies used and technical considerations for the generation of ordered‐clone fingerprint maps.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.236
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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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