Clothing resistance and potential evapotranspiration as thermal climate indicators—The example of the Carpathian region
Bibliographic record
Abstract
Abstract Clothing resistance parameter rcl 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. rcl is simulated by a model based on clothed human body energy balance considerations. rcl 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 rcl is linked to PET. The annual mean of rcl is statistically interconnected with annual sum of PET, and the annual fluctuation of rcl (drcl = rclmax − rclmin) with the annual fluctuation of PET (dPET = PETmax − PETmin). The Carpathian region's thermal climate is analysed by comparing PET results with rcl model results and rcl results obtained by statistical link. We showed that rcl 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 rcl and drcl values obtained by statistical link is comparable to the spatial heterogeneity of PET, but is lower than that obtained from the rcl model. Similarly to rcl model results, rcl results obtained by statistical link are also sensitive to human body somatotype variations. All these results suggest that statistical connections between rcl and PET and drcl 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 rcl, which is obtained by both the model and the statistical link.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".