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Record W4412615097 · doi:10.15690/pf.v22i3.2866

Child Nutrition Problems and Measures of the Struggle During the Great Patriotic War (1941–1945) (to the 80 Years of Victory in the Great Patriotic War)

2025· article· en· W4412615097 on OpenAlexaff
С. А. Шер, Valery Yu. Albitskiy

Bibliographic record

VenueПедиатрическая фармакология · 2025
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsVictoryHistoryAncient historyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

The article presents the results of a historical and medical study reflecting the problems of child food during the Great Patriotic War of 1941–1945. The purpose of the study is to conduct an objective analysis of the situation related to the shortage of child food in the most difficult period of the history of our country and give an impartial assessment of the state measures taken to solve the problems that have arisen. An analysis of archival and literary sources indicates that providing children with food during the war was one of the most difficult tasks of high-priority importance. Deprived of vital nutrients and vitamins that guarantee normal growth and development processes, children of war suffered from hypotrophy, hypovitaminosis, rickets, they had а physical and psychomotor retardation, a decrease of the body resistance. Despite the extraordinary circumstances, a set of state measures was implemented to ensure the need of the child’s body for the necessary vital nutrients and vitamins. However, some territories had difficulties of an objective and organizational nature with the supply of food to children. To save the lives of young children, their normal physical and psychomotor development, reduce morbidity and mortality, scientists conducted research in order to obtain alternative food sources rich in proteins, fats, carbohydrates and vitamins that replace children’s foods that were scarce during the war.

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 score0.151
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.255
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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