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Record W3092432635 · doi:10.1186/s13104-020-05321-w

An ecological study of the association between environmental indicators and early childhood caries

2020· article· en· W3092432635 on OpenAlexafffund
Morẹ́nikẹ́ Oluwátóyìn Foláyan, Maha El Tantawi, Balgis Gaffar, Robert J. Schroth, Jorge L. Catillo, Ola B. Al‐Batayneh, Arthur Kemoli, A. Carolina Medina Díaz, Verica Pavlić, Maher Raswhan

Bibliographic record

VenueBMC Research Notes · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Manitoba
FundersAlexandria UniversityCanadian Institutes of Health ResearchJordan University of Science and TechnologyQueen Mary University of London
KeywordsMedicineEmission intensityNitrous oxideCarbon dioxideEcological studyEnvironmental healthDemographyEcologyEnvironmental scienceBiologyChemistryPopulation

Abstract

fetched live from OpenAlex

Abstract Objectives A prior study described the association between ecosystem vitality, environmental health, and early childhood caries (ECC). The objective of this study was to determine the association between 24 global environmental indicators and ECC in 3–5-year-old children. Results In 61 countries, 55.5% of 3–5-year-old children had ECC. Eight factors had a small effect-size association with ECC: percentage of area that is marine-protected (partial eta squared; ƞ 2 = 0.03); species habitat index (ƞ 2 = 0.06); percentage of tree-cover loss (ƞ 2 = 0.03); regional marine trophic index (ƞ 2 = 0.03); total carbon dioxide emission intensity (ƞ 2 = 0.03); methane emission intensity (ƞ 2 = 0.04); nitrous oxide emission intensity (ƞ 2 = 0.06); and sulfur dioxide emission intensity (ƞ 2 = 0.03). Regression analysis revealed that two of these factors were significantly associated with the prevalence of ECC: methane emission intensity was inversely associated with ECC prevalence (B = − 0.34, 95% CI = − 0.66, − 0.03; p = 0.03), and nitrous oxide had a direct association with ECC prevalence (B = 0.35, 95% CI = 0.04, 0.67; p = 0.03).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.227

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 head0.076
GPT teacher head0.350
Teacher spread0.274 · 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 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

Citations13
Published2020
Admission routes2
Has abstractyes

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