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Record W1791605361 · doi:10.1002/9780470027318.a0536

Nucleic Acid Analysis in Clinical Chemistry

2000· other· en· W1791605361 on OpenAlexaff
Michael Thompson, Anil K. Deisingh

Bibliographic record

VenueEncyclopedia of Analytical Chemistry · 2000
Typeother
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNucleic acidComputational biologyPolymerase chain reactionSingle-nucleotide polymorphismGeneticsBiologyChemistryMedicineGeneGenotype

Abstract

fetched live from OpenAlex

Abstract This article examines the role of nucleic acid analysis in clinical chemistry. It is a broad‐based article and begins with the background knowledge which will be needed to appreciate the succeeding sections. In the Introduction, structure and function of nucleic acids, hybridization, probe labeling, hybridization protection, polymerase chain reaction (PCR) and sequencing are discussed. Major sections are devoted to microbiological analysis (with particular emphasis on bacteriology), genetic diseases and screening [discussions are provided for cystic fibrosis (CF), heriditary fructose intolerance (HFI) and hemochromatosis along with an introduction to the Human Genome Project and single nucleotide polymorphisms (SNP)], other areas of human medicine (oncology, colorectal cancer and Alzheimer's disease (AD) are considered) and the biosensor approach. A discussion of interesting current and future developments, not only in nucleic acid analysis, but also in gene therapy, concludes the article.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0220.018

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 head0.007
GPT teacher head0.267
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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
Published2000
Admission routes1
Has abstractyes

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