Evaluation of thermal manikin correlation ensembles
Notice bibliographique
Résumé
The risk of accidental immersion in cold water is one that many people who work on, or travel over, are exposed to every day. Sudden immersion in cold water can produce a series of physiological responses, termed the cold shock response, that can be fatal, and prolonged immersion can result in hypothermia. Immersion suits are life saving appliances that are designed to protect against the cold shock response, and delay the onset of hypothermia. In order to ensure they provide a sufficient level of thermal protection against cold water, immersion suits are certified to various international standards. While human participants can be used to assess the thermal protection of immersion suits in these standards tests, they are physically gruelling, and potentially ethically questionable to do. Thermal manikins offer an alternative to using human participants, but are not accepted by the International Maritime Organization until they show that they correlate satisfactorily to human results. Previous work has correlated two thermal manikins across a range of commercially available immersion suits, and suggested the use of correlation ensembles to help with correlating various thermal manikins around the world. To contribute to the growing knowledge base establishing this correlation, Transport Canada requested the National Research Council Canada to perform tests with thermal manikins to investigate the viability of correlation ensembles. Two thermal manikins, TIM and NEMO, were tested in four separate correlation ensembles. Each correlation ensemble consisted of different garments such as long underwear and fleece pile suits to achieve a specific thermal insulation (clo) value. Both thermal manikins performed at least two immersion tests in each correlation ensemble in the same conditions in the National Research Council Canada’s Thermal Measurement Lab. After dressing the thermal manikin in the ensemble, it was secured to a metal stretcher and immersed in ~5°C stirred water. Each test lasted until at least 30 minutes of thermal steady state data was recorded. Across all four correlation ensembles (Correlation Ensembles 1-4), NEMO measured a higher clo value compared to TIM. For Correlation Ensemble 1, TIM reported a mean [SD] clo value of 0.875 [0.035] clo, and NEMO reported a mean clo value of 1.173 [0.041] clo. For Correlation Ensemble 2, TIM reported a mean clo value of 0.530 [0.028], while NEMO reported a mean co value of 0.715 [0.019] clo. For Correlation Ensemble 3, TIM reported a mean clo value of 0.285 [0.021], while NEMO reported a mean clo value of 0.411 [0.017] clo. For Correlation Ensemble 4, TIM reported a mean clo value of 0.140 [0.000], while NEMO reported a mean clo value of 0.202 [0.006] clo. Across all four ensembles, there was a very strong correlation (r = 0.998) between the two thermal manikins. The results from this study show that the concept of correlation ensembles is valid. When tested according to a standardized methodology, there was excellent agreement between the two thermal manikins using the correlation ensembles. While there were absolute differences between the two thermal manikins, with NEMO measuring higher clo values compared to TIM, the strong correlation between the two allows for the results from one manikin to be referenced to the other. As a result, the use of correlation ensembles could allow for any thermal manikin to be correlated back to one that has been compared to human tests. This would ensure that any immersion suit certification tests performed with any thermal manikin, that has been tested with the correlation ensembles, can be correlated back to human results. Correlating thermal manikin results back to human tests would allow for increased confidence that any immersion suit tested with manikins would indeed provide a sufficient level of protection from cold water, increasing the safety of people who may need to rely on them.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».