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Record W3124723440 · doi:10.1093/wber/lhaa018

Caloric Intake and Energy Expenditures in India

2020· article· en· W3124723440 on OpenAlexaff
Shari Eli, Nicholas Li

Bibliographic record

VenueThe World Bank Economic Review · 2020
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCaloric theoryFalling (accident)Energy expenditureAnthropometryEconomicsCaloric intakePopulationDemographyCalorieDemographic economicsBody weightMedicineEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

Abstract Total energy expenditures for the Indian population between 1983 and 2012 are estimated to shed light on the debate concerning falling measured caloric intake during the period (A. Deaton and J. Drèze. 2009. “Food and Nutrition in India: Facts and Interpretations.” Economic and Political Weekly 44(7): 42–65). Anthropometric, time-use, and detailed employment surveys are used to estimate the separate components of total energy expenditure related to metabolism and physical activity levels. Despite a significant drop in adult physical activity levels, total energy expenditures are flat overall between 1983 and 2012. Rising metabolic requirements due to increases in weight dampened the effect of falling activity levels on total energy expenditure. In addition, the 10 percent decline in the population share of children in the period raised average total energy expenditures considerably as children have much lower metabolic requirements and activity levels than adults.

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: none
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.269
Teacher spread0.247 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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