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Record W2809127135 · doi:10.1002/kin.21194

On the hydrolysis mechanisms of amides and peptides

2018· article· en· W2809127135 on OpenAlexaff
Allan L. L. East

Bibliographic record

VenueInternational Journal of Chemical Kinetics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsChemistryHydrolysisAmideFormamideLimitingGibbs free energyCatalysisHydrolysis constantConfusionComputational chemistryProteasesReaction rate constantCombinatorial chemistryOrganic chemistryKineticsThermodynamicsEnzyme

Abstract

fetched live from OpenAlex

Abstract Here the possibility is raised that peptide hydrolysis, in the absence of catalysis by proteases or buffers, may still have a self‐catalyzing mechanism that differs from ordinary amide hydrolysis. Second, an attempt is made to clarify the ongoing confusion in the computational chemistry literature regarding the rate‐limiting step in ordinary amide hydrolysis. Third, Gibbs activation energies (free‐energy barriers) for formamide hydrolysis are derived from rate constants and presented under different concentration conventions, for ease of comparison to values from computational chemistry predictions past and future.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.164

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 head0.005
GPT teacher head0.238
Teacher spread0.232 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations29
Published2018
Admission routes1
Has abstractyes

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