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Record W1588747168 · doi:10.1002/0471140864.ps1109s58

Amino Acid Analysis

2009· article· en· W1588747168 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Protocols in Protein Science · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsHealth Canada
Fundersnot available
KeywordsAmino acid analysisAmino acidHydrolysisPerformic acidChemistryAcid hydrolysisPeptideMethionineCysteineLysineTryptophanPeptide bondBiochemistryOrganic chemistryEnzyme

Abstract

fetched live from OpenAlex

Amino acid analysis is used to determine the amino acid content of amino acid-, peptide- and protein-containing samples. With minor exceptions, proteins are long linear polymers of amino acids connected to each other via peptide bonds. The first step of amino acid analysis involves hydrolyzing these peptide bonds. The liberated amino acids are then separated, detected, and quantified. The method was first developed by Moore, Stein and coworkers in the 1950s using HCl acid hydrolysis, and, despite considerable effort by many workers, the basic methodology remains relatively unchanged. This unit provides an overview and strategic planning for amino acid analysis, discussing a range of methodologies and issues. In addition, several common methods used for analysis of L-amino acids are described in detail, including: HCl acid hydrolysis, performic acid oxidation for methionine and cysteine analysis, base hydrolysis for tryptophan analysis, analysis of free amino acids, and analysis of reactive lysine.

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.

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)0.001
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 score0.086
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.357
Teacher spread0.334 · 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