Spatial and temporal patterns of solar radiation based on topography and air temperature
Bibliographic record
Abstract
Incident solar radiation is a driving force for many ecological and hydrological processes. For this study, we developed TopoRad, a new radiation model, to describe spatial and temporal patterns of daily radiation based on topography and daily temperature regimes. The model was applied to the Mount Jumbong Forest, located in the mid-eastern area of the Korean peninsula; and the model calculations were evaluated by varying the spatial scales of the digital elevation models (DEMs). In the TopoRad, a clearness index was used to calculate global radiation on a horizontal surface and to partition direct and diffuse radiation. Topographic corrections were separately calculated for each direct and diffuse radiation, using daily topographic modifiers calculated from a DEM. TopoRad predicted daily global radiation of five weather stations with a mean absolute error of 3.1 MJ·m2·day1 and a mean bias of 0.3 MJ·m2·day1. In the spatial application for Mount Jumbong Forest, distinctively different patterns between direct and diffuse radiations were found where direct radiation (5.2 MJ·m2·day1) had more influence than diffuse radiation (4.6 MJ·m2·day1) on annual mean daily radiation. When the scaling effect was inspected across different spatial resolutions, the predicted global radiation was nonlinearly related to spatial resolutions. As the spatial resolution became more coarse, the predicted radiation decreased for south-facing slopes and increased for north-facing slopes, indicating that the predictions from the models cannot be generalized for gradients. TopoRad is better suited to predict daily radiation in rugged landscapes where fine-scale prediction is required.
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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) | 0.000 | 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".