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Record W19037577 · doi:10.1007/s00239-008-9182-5

A Study of rhythm in London: is syllable-timing a feature of multicultural London English?

2011· article· en· W19037577 on OpenAlexaff
Eivind Torgersen, Anita Szakay

Bibliographic record

VenueJournal of Molecular Evolution · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSyllableMulticulturalismLinguisticsRhythmNarrativeEthnic groupPsychologyFeature (linguistics)Varieties of EnglishHistorySociologyArt

Abstract

fetched live from OpenAlex

Although thousands of in vitro selection and evolution experiments have been performed to seek different types of targets, most of them have only inspected the terminal evolutionary pool for patterns. In addition, to rapidly obtain the most favorable target, many experiments have been carried out under increasing selection pressure. However, increasing selection pressure seldom occurs in natural evolution. We studied the dynamic features of DNA in vitro evolution in the presence of the Mnt repressor under sequential constant selection pressure. When evolving under a constant pressure from an initial random pool of DNA, our system showed a clear, sharp, and reproducible crossover from a random population to an advantageous population (higher binding affinities of DNA sequences to the Mnt repressor). This crossover occurs after a long latent period during which there are no obvious changes in the population phenotype. We demonstrated that the existence of the crossover is caused by a significant sequence-nonspecific binding in the repressor-DNA system. After the crossover, the population settled in a stationary distribution of genotypes, which responded immediately to a subsequent sudden increase in selection pressure. We also experimentally tested the linear correlation between the evolution speed and sequence diversity (Fisher's theorem) in our system.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.029
GPT teacher head0.294
Teacher spread0.264 · 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 designQualitative
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

Citations6
Published2011
Admission routes1
Has abstractyes

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