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Record W36613519 · doi:10.3390/ijms24010081

Promoting Emergent Literacy and Numeracy through Quality Children's Literature.

2002· article· en· W36613519 on OpenAlexfundno aff
Patricia Dickinson

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2002
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsNumeracyLiteracyQuality (philosophy)PsychologySociologyPedagogy

Abstract

fetched live from OpenAlex

Polymorphisms in taste receptor genes have been shown to play a role in early childhood caries (ECC), a multifactorial, biofilm-mediated disease. This study aimed to evaluate associations between severe-ECC (S-ECC), the oral microbiome, and variants in genes that encode components of the G protein-coupled receptor (GPCR) signaling cascade involved in taste sensation. A total of 176 children (88 caries-free; 88 with S-ECC) were recruited. Analyses of <i>16S</i> and <i>ITS1 rRNA</i> microbial genes and seven (<i>GNAQ, GNAS, GNAT3, GNAI2, RAC1, RALB</i>, and <i>PLCB2</i>) human genes were pursued using next-generation sequencing. Regression analyses were performed to evaluate associations between genetic variants, S-ECC, and the supragingival plaque microbiome. Results suggest that <i>PLCB2</i> rs2305645 (T), rs1869901 (G), and rs2305649 (G) alleles had a protective effect on S-ECC (rs2305645, odds ratio (OR) = 0.27 (95% confidence interval (CI): 0.14-0.51); rs1869901, OR = 0.34 (95% CI: 0.20-0.58); and rs2305649, OR = 0.43 (95% CI: 0.26-0.71)). Variants in <i>GNAQ, GNAS, GNAT3, PLCB2, RALB</i>, and <i>RAC1</i> were associated with oral fungal and bacterial community composition. This study revealed that three loci at <i>PLCB2</i> are significantly associated with S-ECC. Variants in multiple genes were associated with the composition of dental biofilm. These findings contribute to the current knowledge about the role of genetics in S-ECC.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.034
GPT teacher head0.376
Teacher spread0.341 · 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 designTheoretical or conceptual
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

Citations0
Published2002
Admission routes1
Has abstractyes

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