Hydrological and watershed characteristics of the El‐Kabir River, North Lebanon
Bibliographic record
Abstract
Abstract The El‐Kabir River watershed is the largest in western Lebanon and is shared between Lebanon and Syria. The river forms most of the northern boundary of Lebanon with Syria, being characterized by water flow throughout the year. The characteristics of the river and its variable hydrologic properties are the result of abrupt changes in land physiography. Until recently, data on Lebanese rivers was inadequate, especially for rivers shared with other countries. The El‐Kabir River watershed typifies this situation, particularly when the river has undergone many changes, including water pollution and declining discharge because of changing climate and increased pollution. This study was implemented in the context of a large investigation of the watershed which was funded by the International Development Research Council, Canada, for the purpose of improving the baseline data and knowledge required to effectively manage this important resource. Within the water cycle, ≈ 250 × 106 m3 of precipitation falls on the Lebanese side. Of this volume, ≈ 50% is lost as evaporation and transpiration, while 5–50% of the remainder infiltrates to ground water, with the residual becoming land run‐off. An obvious decline of ≈ 40% of the total river discharge of the river has occurred over the last 50 years. It can be explained by climate change and by water extraction associated with dramatic increases in population and associated land uses. The hydraulic configuration and characteristics of the river have two major orientations; namely, NE–SW and E–W. These orientations are the product of geological structure and lithologies. Furthermore, each has different hydrologic properties related to watershed size, elevation, slope, catchment shape and orientation, although both orientations are directly inter‐related.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".