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Record W3094530175 · doi:10.29173/topo33

The Skinny on Obesity: A need for a comprehensive diagnostic of obesity beyond self and health variables to include social and environmental strategic drivers

2017· article· en· W3094530175 on OpenAlexvenueno aff

Bibliographic record

VenueTopophilia · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPremiseObesityAction (physics)Built environmentVariable (mathematics)DiseaseEnvironmental healthHuman obesityPsychologyMedicineEcologyBiology

Abstract

fetched live from OpenAlex


 
 
 The rapid increase in the number of obese adults and children in both developed and developing countries is alarming and will strain health resources in the future. A review of pertinent social and built environmental influences that contribute to the prevalence of this chronic disease is examined with reference to current literature. This paper explores the relationship between factors of the built and social environments that ultimately lead to the creation of obesogenic environments. Recognizing the importance of human interaction, coupled with genetic factors, with the built environment in addressing obesity is an important variable, the author argues that rather than evaluating obesity with a retrospective approach, a forward thinking approach in creating built environments which entice human action in the environment should be an ongoing premise in fighting obesity.
 
 

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.003
Science and technology studies0.0030.011
Scholarly communication0.0070.016
Open science0.0020.009
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0060.001

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 head0.097
GPT teacher head0.409
Teacher spread0.312 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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