Bibliographic record
Abstract
In this review, a summary of documented research on immersion suits (i.e. constant wear, abandonment suit and diving suit) is presented. Particular emphasis was placed on research regarding the performance and analysis of these protective suits. Heat loss from the human body is critical for the protection of the wearer of the suit during cold water immersion, while thermal stress can be experienced by the pilots or helicopter rescuers when wearing immersion suits under normal or hot environmental conditions. In addition, the knowledge gaps have been identified in the aspects of environmental hazards, development of novel textile materials, garment design features and test methods. The key factors that are fundamental to thermal insulation of immersion suits have been summarized. Efforts for improving thermal insulation have been presented. Three-dimensional body scanning, as a new approach being used to understand and improve fit and sizing of garment, may contribute to a better understanding of thermal protection and thermal comfort of immersion suits. The simulation of real open sea scenario to test thermal performance of textiles poses a big challenge for researchers. This article reviews what is known about immersion suits, describes future development trends and identifies domains for improving performance of immersion suits and testing methods.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".