The Effect of Face Topography on Frostbite
Bibliographic record
Abstract
Winters in most countries within the Northern hemisphere, which include heavily populated regions of Russia, Canada, China, and the United States, are commonly known to be harsh, unforgiving, and unpredictable. Cold weather injuries such as frostbite can occur within only a few minutes of exposure to extremely cold temperatures and high wind chill. We seek to provide a quantitative model of the effects of extremely cold and freezing temperature on the face and the extent of damage to tissue over time. In this study, our model will take into account the airflow of cold temperature on the face and the convective heat given off by the face. Using the duo model, we will be able to show the severity of tissue damaged. In this study, we consider both the tissue temperature on the face and the temperature of the airflow. To investigate the mechanism of forced convection heat extraction in the face, we will use COMSOL, a multiphysics finite element analysis and simulation software, to develop a simple geometry of the face and replicate the heat exchanging properties when exposed to extreme temperature conditions. Our model will be a 3D simulation of the face with boundary conditions a close distance away from the face. We are primarily focused on simulating that the airflow is coming directly in front of the face and that is where the primary damage will occur. We will use the data provided by the National Weather Service that demonstrates how quickly hypothermia and frostbite can occur depending on the windchill and temperature. The model will demonstrate the extent of damage that can occur in varied temperature settings. It will simulate how long the body can retain thermal energy while convective heat loss is simultaneously occurring. This model will allow us to demonstrate the importance of preventative care during extreme temperature conditions to avoid frostbite. It will allow us to quantitatively demonstrate how much tissue is damaged to help diagnose and treat frostbite cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".