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Record W4407240644 · doi:10.7554/elife.97982.sa2

Author response: Serum metabolome indicators of early childhood development in the Brazilian National Survey on Child Nutrition (ENANI-2019)

2024· peer-review· en· W4407240644 on OpenAlexaff
Marina Padilha, Victor N. Keller, Paula Normando, Raquel Machado Schincaglia, Nathalia Freitas-Costa, Samary da Silva Rosa Freire, Felipe Mendes Delpino, Inês RR de Castro, Elisa MA Lacerda, Dayana Rodrigues Farias, Zachary Kroezen, Meera Shanmuganathan, Philip Britz‐McKibbin, Gilberto Kac

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMetabolomePsychologyDevelopmental psychologyEnvironmental healthData scienceMedicineBiologyComputer scienceMetabolomicsBioinformatics

Abstract

fetched live from OpenAlex

A panel of serum biomarkers, including dietary and microbial-derived metabolites related to the gut-brain axis, were identified as potential for tracking children at risk of early childhood developmental delays.

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)0.005
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.080
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0800.021

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 head0.037
GPT teacher head0.339
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2024
Admission routes1
Has abstractyes

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