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Record W4220750305 · doi:10.1016/j.jand.2022.03.010

Using Short-Term Dietary Intake Data to Address Research Questions Related to Usual Dietary Intake among Populations and Subpopulations: Assumptions, Statistical Techniques, and Considerations

2022· article· en· W4220750305 on OpenAlexaff
Sharon I. Kirkpatrick, Patricia M. Guenther, Amy F. Subar, Susan M. Krebs‐Smith, Kirsten Herrick, Laurence S. Freedman, Kevin W. Dodd

Bibliographic record

VenueJournal of the Academy of Nutrition and Dietetics · 2022
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStatistical inferencePercentileStatisticsInferenceTerm (time)EstimationVariance (accounting)SkewnessEconometricsPopulationMathematicsMedicineComputer scienceEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.516
metaresearch head score (Gemma)0.657
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.484
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5160.657
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.008
Science and technology studies0.0030.009
Scholarly communication0.0060.007
Open science0.0080.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0020.001

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 head0.356
GPT teacher head0.472
Teacher spread0.116 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations59
Published2022
Admission routes1
Has abstractno

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