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Record W1498069653 · doi:10.3389/fphar.2015.00110

Adipose tissue and sustainable development: a connection that needs protection

2015· article· en· W1498069653 on OpenAlexaff
Angelo Tremblay, Éliane Picard-Deland, Shirin Panahi, André Marette

Bibliographic record

VenueFrontiers in Pharmacology · 2015
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsLipotoxicityContext (archaeology)ProductivityObesityPsychosocialEnvironmental healthOverweightAffect (linguistics)Adipose tissueMedicineNatural resource economicsPsychologyEndocrinologyBiologyPsychiatryEconomic growthEconomics

Abstract

fetched live from OpenAlex

Obesity is generally considered as an excess body fat that increases the risk to develop ergonomic, metabolic, and psychosocial problems. As suggested in this paper, body fat gain is also a protective adaptation that prevents body lipotoxicity, contributes to the secretion of molecules involved in metabolic regulation, and dilutes lipid soluble persistent organic pollutants. Recent literature shows that this protective role of adipose tissue is more solicited in a modern context in which unsuspected factors can affect energy balance to a much greater extent than what is generally perceived by health care professionals. These factors include short sleep duration, demanding mental work, and chemical pollution whose impact is more detectable in a context dominated by economic productivity and competitiveness. Since these factors might also include the increase in atmospheric CO2, it is likely that obesity prevention will need the support of a promotion in sustainable development, whether it is for human health, and well-being or global ecological protection.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0040.005
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 head0.024
GPT teacher head0.287
Teacher spread0.264 · 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
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

Citations5
Published2015
Admission routes1
Has abstractyes

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