Hydromorphometric Analysis Based on Geographical Techniques for the Huraymila Basin in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
The study aims to build a digital geographic information base that provides basic information for effective management of water resources and the development of sustainable strategies for preserving the water basin. The study relied on a quantitative analytical approach by conducting measurements and mathematical equations and relying on Geographical Information Systems (GIS). The area of the basin is 512.10 km 2 , its length is 42.67 km, 19.02 km 2 in width, and the periphery is 141.68 km 2 . The basin takes a rectangular shape with a ratio of 0.6, with 0.28 shape factor. The relief rate reached 7.63 m/km, the roughness value also reached 0.73, the basin consists of 2,115 streams located in six ranks, with a total length of about 1,155 km. As for the density of the lengths of the waterways and the density of their numbers, they reached 2,255 km/km 2 and 546 streams/km 2 . The study recommends establishing a hydrometric monitoring station and linking it with the relevant ministries and departments, as well as expanding agricultural projects using modern geographic techniques that provide sufficient information to manage the water basin and enhance understanding of the hydromorphometric behavior of the Huraymila Basin and other basins with similar characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".