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Record W2202576544 · doi:10.3390/jintelligence3040160

Phenotypic, Genetic, and Environmental Correlations between Reaction Times and Intelligence in Young Twin Children

2015· article· en· W2202576544 on OpenAlexaff
Julie Aitken Schermer, Philip A. Vernon

Bibliographic record

VenueJournal of Intelligence · 2015
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsWestern University
Fundersnot available
KeywordsBivariate analysisDizygotic twinsUnivariateTwin studyDizygotic twinHeritabilityCorrelationGenetic correlationMonozygotic twinPhenotypeBiologyPsychologyGeneticsMultivariate statisticsStatisticsGenetic variationMathematicsMedicineGene

Abstract

fetched live from OpenAlex

Phenotypic, genetic, and environmental correlations between various reaction time measures and intelligence were examined in a sample of six-year-old twin children (N = 530 individuals). Univariate genetic analyses conducted on the same-sex pairs (101 monozygotic (MZ) pairs and 132 same-sex dizygotic (DZ) pairs) demonstrated that the intelligence measure and four of the seven reaction time measures had a genetic component (ranging from 44% to 76%). At the phenotypic level, half of the reaction time measures had significant negative correlations with the intelligence measure. Bivariate genetic analyses revealed that only two of the observed phenotypic correlations could be explained by common genetic factors and that the remaining correlations were better explained by common environmental factors.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.298
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2015
Admission routes1
Has abstractyes

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