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Record W1603991683 · doi:10.1002/cphy.c130047

Thermoregulatory Modeling for Cold Stress

2014· review· en· W1603991683 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

VenueComprehensive physiology · 2014
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCold stressShiveringHypothermiaHeat stressVasomotorComputer sciencePhysiologyChemistryBiologyGeologyAtmospheric sciences

Abstract

fetched live from OpenAlex

Modeling for cold stress has generated a rich history of innovation, has exerted a catalytic influence on cold physiology research, and continues to impact human activity in cold environments. This overview begins with a brief summation of cold thermoregulatory model development followed by key principles that will continue to guide current and future model development. Different representations of the human body are discussed relative to the level of detail and prediction accuracy required. In addition to predictions of shivering and vasomotor responses to cold exposure, algorithms are presented for thermoregulatory mechanisms. Various avenues of heat exchange between the human body and a cold environment are reviewed. Applications of cold thermoregulatory modeling range from investigative interpretation of physiological observations to forecasting skin freezing times and hypothermia survival times. While these advances have been remarkable, the future of cold stress modeling is still faced with significant challenges that are summarized at the end of this overview.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.164
GPT teacher head0.407
Teacher spread0.243 · 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