Air temperature and thermal comfort conditions in mountainous and urban regions
Bibliographic record
Abstract
The present study explored air temperature and thermal comfort conditions in mountainous region of Nafpaktia (Municipality of Apodotia, Prefecture of Aitoloakarnania), a region without important commercial, industrial or other activities in west continental Greece, and in Athens, a metropolitan urban center in southeast continental Greece.There were 11 study sites in the case of Nafpaktia (676-1455 m altitude), areas with different altitude, ground cover, plant species and plant density.There was one study site in Athens (30 m altitude) nearby a densely built area, under the pressure of industrial activities and heavy traffic.Air temperature and humidity, 1.5 m above ground surface, were monitored simultaneously every 15 min in each site for the period between 1 July and 31 August 2006.Air temperature and relative humidity data were used for the calculation of the thermohygrometric index (THI) from which thermal comfort conditions were evaluated.For this work, two sites of mountainous Nafpaktia region (MNR), one characterized by the lower (fir and chestnut forest area) and the other by the higher (Evinos River area) average THI value, are presented.Results showed that MNR provided clearly improved air temperature and thermal comfort conditions in comparison to Athens in the study period.Fir and chestnut forest area was more beneficial, from a bioclimatological point of view, compared to Evinos River area.Therefore, fir and chestnut forest area should be further supported as a reliable alternative tourist destination for rest and recreation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".