Accelerated Drying of Wet Boots
Bibliographic record
Abstract
Much has been written about materials known as 'super absorbers' with respect to their ability to keep the skin dry in the presence of moisture. One such material is sodium polyacrylate. Because recent field trials with Canadian Forces soldiers have reconfirmed that donning wet combat boots is very uncomfortable, a study was done to assess the efficacy of using sodium polyacrylate based drying pads to dry wet combat boots in a simulated field environment. The boot used in this study was non-insulated, had a water resistant full grain leather upper, and lined with a waterproof, water-vapour permeable membrane covered with a nylon inner liner. A dry boot and pad were weighed and the boot was wetted inside and out. After the boot was removed from the water and the water poured out of the boot, a drying pad was placed inside the boot and the entire system was placed on electronic scales connected to a computer. The wetting characteristics of the pad and the drying characteristics of the boot were monitored and analysed at two temperatures, 15.8 deg. C and 23.3 deg. C. The pad absorbed water very quickly, within about 30 minutes. It does not, however, pick up very much moisture, 62.9 to 78.9 grams. It is postulated that a soldier will require at least 4-6 pads for drying his/her wet weather boots in the field.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".