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Record W2046262977 · doi:10.2298/tsci0701075r

A novel method for estimating the entropy generation rate in a human body

2007· article· en· W2046262977 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal Science · 2007
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEntropy productionEntropy (arrow of time)Human bodyEmissivityEnvironmental scienceThermodynamicsRelative humidityMechanicsMaterials sciencePhysicsOpticsBiologyAnatomy

Abstract

fetched live from OpenAlex

The main objective of this study is to show a method for calculating entropy generation (Sgen) in a human body under various environmental and physiological conditions. The Sgen in a human body is a measure of activeness of motions, reactions, and irreversibility of processes occurring in a body and is a kind of holistic and thermodynamic index, which characterizes a human body as a whole. Human body at healthier and normal condition generates the least amount of Sgen. Heat transfer over a human body, activity (at rest, Sgen = 0.21J/sK or exercise, Sgen=2.19 J/sK or at death, Sgen = 0J/sK), ambient, body and mean radiant temperatures, emissivity and absorbity of human skin, internal heat elimination, body weight and height, and air speed effect much more on the Sgen in a human body compared to the effects of mass exchange into and out of the body, internal heat production, cross-sectional area of human body, clothing, altitude, and relative humidity of the surrounding air. Among these factors entropy production due to heat transfer over a human body plays a significant role in the total entropy generation rate. .

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.418
Teacher spread0.334 · 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