Thermal Environment Assessment Reliability Using Temperature —Humidity Indices
Bibliographic record
Abstract
A reliable assessment of the thermal environment should take into account the whole of the six parameters affecting the thermal sensation (air temperature, air velocity, humidity, mean radiant temperature, metabolic rate and thermo-physical properties of clothing). Anyway, the need of a quick evaluation based on few measurements and calculations has leaded to like best temperature-humidity indices instead of rational methods based on the heat balance on the human body. Among these, Canadian Humidex, preliminarily used only for weather forecasts, is becoming more and more widespread for a generalized assessment of both outdoor and indoor thermal environments. This custom arouses great controversies since using an index validated in outdoor conditions does not assure its indoor reliability. Moreover is it really possible to carry out the thermal environment assessment ignoring some of variables involved in the physiological response of the human body? Aiming to give a clear answer to these questions, this paper deals with a comparison between the assessment carried out according to the rational methods suggested by International Standards in force and the Humidex index. This combined analysis under hot stress situations (indoor and outdoor) has been preliminarily carried out; in a second phase the study deals with the indoor comfort prediction. Obtained results show that Humidex index very often leads to the underestimation of the workplace dangerousness and a poor reliability of comfort prediction when it is used in indoor situations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".