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Record W3038770726 · doi:10.1596/1813-9450-9309

Caloric Intake and Energy Expenditures in India

2020· book· en· W3038770726 on OpenAlexaff
Shari Eli, Nicholas Li

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2020
Typebook
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCaloric theoryFalling (accident)EconomicsCaloric intakeDemographic economicsAgricultural economicsDemographySocioeconomicsDevelopment economicsMedicineEnvironmental healthBody weightSociology

Abstract

fetched live from OpenAlex

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 (Deaton and Dre ze 2009). 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.938
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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