The time-in-daylight land-surface parameter
Bibliographic record
Abstract
Time-in-Daylight (TiD) estimates the portion of total daylight over a time span that a location experiences direct radiation. This paper describes a method for estimating TiD using horizon angle maps derived in a range of azimuths and information about the sun’s position during the time span. TiD is evaluated as a potential land-surface parameter (LSP) for relief mapping and solar radiation modelling applications. The use of horizon angle to map shadow areas in calculating TiD makes this LSP conceptually similar to both openness and sky-view factor (SVF). However, TiD differs most significantly in the pairing of horizon angle maps with a dynamic model of sun position. The findings showed that TiD is well suited to applications in relief visualization, particularly with digital surface models (DSMs) in urban areas. The ability to estimate TiD with specific date/time ranges also makes it better suited for solar radiation modelling applications than either openness or SVF.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.039 |
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; both teacher heads agree on what is shown here.
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".