Bibliographic record
Abstract
Abstract Even as better materials are developed for protective clothing, NFPA data indicate the number of burn related fire fighter deaths and severe injuries is increasing. These injuries are unexpected for the most part. As a result, questions arise about Thermal Protective Performance (TPP) ratings and whether performance changes are due to use or the effects of aging. As part of a NIST-sponsored Small Business Innovative Research project, current TPP test techniques were evaluated. Some changes and extensions are suggested. In TPP tests, dry fabric samples or ensembles are exposed to heat flux of 83 kW/m2 with nominally 50% radiative and 50% convective heat transfer. Heat transmission through the test sample is measured with a copper calorimeter. The TPP Rating is the time in seconds required for a 2nd degree burn. Current problems include: 1) Current test methods overestimate time to 2nd degree burn. 2) No information is provided on maximum potential burn damage. 3) No information is provided on heat transfer or fabric properties. 4) No information is provided on how performance changes with use. Suggested changes and extensions include: 1) Change the heat source — use a modified radiant protective performance technique. 2) Replace the copper calorimeter with a thermal skin simulant sensor. 3) Estimate burn damage using a two step analysis method. 4) Obtain both dry and damp thermal properties for modeling clothing 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".