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Record W1487309080 · doi:10.1074/jbc.x300008200

Leon Heppel and the Early Days of RNA Biochemistry

2003· article· en· W1487309080 on OpenAlexaboutno aff
Maxine Singer

Bibliographic record

VenueJournal of Biological Chemistry · 2003
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicChemical Reactions and Isotopes
Canadian institutionsnot available
Fundersnot available
KeywordsDNARNACytosineGuanineThymineMolecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic AcidNucleic acidBase pairNucleic acid structureChemistryBiochemistryNucleotideGeneticsBiologyComputational biologyGene

Abstract

fetched live from OpenAlex

Today, machines turn out the sequence of a million DNA bases in a day. Fifty years ago, when the chemical and biochemical tools for studying DNA and RNA were at best rudimentary, such a machine was unimaginable. Then, the cutting edge was Erwin Chargaff's demonstration, in 1948, that the base composition of DNA could be reliably determined. His discovery that all DNAs contain equal amounts of adenine and thymine and similarly of guanine and cytosine depended on applying two recent developments: partition chromatography and the absorption spectra of nucleic acid constituents.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0100.007

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 head0.077
GPT teacher head0.376
Teacher spread0.300 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

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