Clothing resistance and potential evapotranspiration as thermal climate indicators—The example of the Carpathian region
Bibliographic record
Abstract
Abstract Clothing resistance parameter r cl and potential evapotranspiration (PET), a major component of Thornthwaite type climate classifications, are used as thermal climate indicators for characterizing the thermal climate of the Carpathian region. r cl is simulated by a model based on clothed human body energy balance considerations. r cl refers to a walking human in outdoor conditions, whose somatotype can differ. Somatotype shapes are determined by applying the Heath–Carter somatotype method. PET is estimated using only air temperature and latitude as inputs. In addition r cl is linked to PET. The annual mean of r cl is statistically interconnected with annual sum of PET, and the annual fluctuation of r cl ( dr cl = r cl max − r cl min ) with the annual fluctuation of PET (dPET = PET max − PET min ). The Carpathian region's thermal climate is analysed by comparing PET results with r cl model results and r cl results obtained by statistical link. We showed that r cl model results are strongly sensitive to human body somatotype variations. It is also shown that the spatial heterogeneity of thermal climates is the lowest in the lowlands and the highest in the mountains. The spatial heterogeneity of r cl and dr cl values obtained by statistical link is comparable to the spatial heterogeneity of PET, but is lower than that obtained from the r cl model. Similarly to r cl model results, r cl results obtained by statistical link are also sensitive to human body somatotype variations. All these results suggest that statistical connections between r cl and PET and dr cl and dPET can be used as subunits in Thornthwaite type climate classifications to obtain human thermal climate information. Lastly, areas with the largest thermal contrast are reproduced in terms of both the annual sum of PET and the r cl , which is obtained by both the model and the statistical link.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".