Orographic Precipitation Modeling with Multiple Linear Regression
Bibliographic record
Abstract
A multiple linear regression (MLR) model, in conjunction with Geographic Information Systems technology, was used to derive the relationship between annual precipitation and elevation, longitude, and latitude. The island of Crete, in Greece, was used as the case study. A multiscale precipitation analysis was performed on areas ranging from large areas (the whole island and the northern, southern, and eastern parts of the island), to medium areas (watersheds), to small areas (sub-basins). While the MLR annual precipitation estimates (which used elevation, latitude, and longitude information) were found to be more reasonable than estimates obtained using elevation only when applied to the whole island, the difference between the MLR estimates and the elevation-only estimates was smaller when applied to individual watersheds. The MLR provides realistic estimates for mean areal precipitation for the island of Crete: 700±100, 950±150, and 1,300±200 mm for dry, average, and wet years, respectively. Elevation-rainfall gradients are: 0.45–0.6, 0.6–0.9, and 0.9–1.3 mm/m for dry, average, and wet years, respectively. Of this, 44% falls on the northern, 33% on the southern, and 23% on the eastern parts of the island for a typical average year.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".