Thermal protective performance of protective clothing used for low radiant heat protection
Bibliographic record
Abstract
A laboratory simulation was performed to study the thermal protective performance of fabric systems under low level thermal hazards in the range of 6.3—8.3 kW/m 2 . Two approaches were used. The first used a method similar to the ASTM F 1939, radiant heat resistance test, while the second used a modification designed to capture the contribution to skin burn injury due to energy stored in the test specimens being released after the direct exposure had ended. Both dry and wet specimens were tested. In order to accommodate the prolonged exposure time a water cooled heat flux sensor was used to calibrate the radiant heat source and measure the energy directly transmitted through during the exposure and discharged later from the fabric systems. The Henriques Burn Integral (HBI) was adopted and programmed with a three layer skin model to predict the time required to achieve a second degree skin burn injury. The study investigated the thermal protection provided by the clothing with different layering and examined the effect of moisture under low level radiant heat exposures. In addition, the physiological burden associated with wearing the clothing was predicted and compared. The results obtained show the difference in measured protection level under low radiant heat from these two approaches and demonstrate that the stored thermal energy released from the clothing system significantly lowers the measured thermal protective performance.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".