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Record W7135775311

Food intake of young children

2012· dissertation· cs· W7135775311 on OpenAlexaboutno aff
Barbora Schimperková

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2012
Typedissertation
Languagecs
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
Fundersnot available
KeywordsFood intakeWeaningQuarter (Canadian coin)Eating behaviorFeeding behaviorPublic healthHealthy eating
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Early childhood is a critical phase for shaping basic eating behaviors and food preferences that impact an individual long-term physical and mental condition. A little child takes over eating habits from his or her parents and their improvement later in life is difficult. Methods: An analysis of eating habits of Czech children aged 12-23 months. The data was obtained in a cross-sectional retrospective questionnaire study. The questionnaires were completed by children's mothers addressed in public places. Results: Almost a quarter (24.4%) of mothers still breast-fed a child after his or her first year of life and the toddlers were fed at average 4.3 times a day. The average age of child when his or her mother stopped its breast-feeding was 8.5 months. The most frequent reason of weaning the child was a paucity or loss of breast milk. More than third (38.5%) of children received the non-milk fluids before the end of the 6th month. And approximately one-third (27.8%) of children obtained the non-milk food before the end of the 6th month. Toddlers got the meals at average 5.3 times a day. Most (83.3%) of mothers had no difficulties with children's feeding. The most frequent eating problem was pickiness. In 28.0% of families the parents let the TV turned on during the mealtime and 58.3% of...

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)0.000
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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