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Record W2273182539 · doi:10.1016/j.acap.2016.01.003

Association Between Meat and Meat-Alternative Consumption and Iron Stores in Early Childhood

2016· article· en· W2273182539 on OpenAlexafffund
Kelly Cox, Patricia C. Parkin, Laura N. Anderson, Yang Chen, Catherine S. Birken, Jonathon L. Maguire, Colin Macarthur, Cornelia M. Borkhoff, Kawsari Abdullah, Imaan Bayoumi, Catherine S. Birken, Sarah Carsley, Yang Chen, Mikael Katz-Lavigne, Kanthi Kavikondala, Christine Koroshegyi, Christine Kowal, Grace Jieun Lee, Dalah Mason, Jessica Omand, Navindra Persaud, Meta van den Heuvel, Peter Wong, Weeda Zabih, Jillian Baker, Tony Barozzino, Joey Bonifacio, Douglas M. Campbell, Sohail Cheema, Brian Chisamore, Karoon Danayan, Paul Das, Mary Beth Derocher, Anh Do, Michael W. Dorey, Sloane Freeman, Keewai Fung, Charlie Guiang, Curtis Handford, Hailey Hatch, Sheila Jacobson, Tara Kiran, Holly Knowles, Bruce Kwok, Sheila Lakhoo, Margarita Lam-Antoniades, Eddy Lau, Fok‐Han Leung, Jennifer Loo, Sarah Mahmoud, Rosemary Moodie, Julia Morinis, Sharon Naymark, Patricia Neelands, James S. Owen, Michael Peer, Marty Perlmutar, Andrew Pinto, Michelle Porepa, Nasreen Ramji, Noor Ramji, Alana Rosenthal, Janet Saunderson, Rahul Saxena, Michael Sgro, Susan Shepherd, Barbara Smiltnieks, Carolyn Taylor, Thea Weisdors, Sheila Wijayasinghe, Ethel Ying, Elizabeth Young, Mark Feldman, Moshe Ipp, Kathleen Abreo, Dharma Dalwadi, Tarandeep Malhi, Antoinetta Pugliese, Megan Smith, Laurie Thompson

Bibliographic record

VenueAcademic Pediatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenPublic Health OntarioCentre for Global Health ResearchUniversity of Toronto
FundersInstitute of Nutrition, Metabolism and DiabetesSick Kids FoundationCanadian Institutes of Health ResearchHospital for Sick ChildrenInstitute of Human Development, Child and Youth HealthSt. Michael's Hospital Foundation
KeywordsConsumption (sociology)Association (psychology)Food scienceEnvironmental healthMedicinePsychologyChemistryArt

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)0.000
metaresearch head score (Gemma)0.002
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations24
Published2016
Admission routes2
Has abstractno

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