Novel Technique for Measuring Erosion in Riverbanks: Tigris River Case at Baiji City-Iraq
Bibliographic record
Abstract
Riverbank erosion is one of the important issues and challenges faced and studied by river management experts.It is one of the main risks that threatens people and structures, as it causes the loss of nearby land from the river.Erosion happens because of hydraulic forces from river water acting on the riverbank or due to the weakening and wearing away of the soil layers that make up the riverbank.A field study of riverbank erosion was carried out over five years at Baiji city along the Tigris River in Salah Al-Din governorate on the right riverbank of the river.Field measurements were conducted from 2020 to the end of 2024, with two measurements and observations carried out each year at different periods to ensure coverage of all the different water levels of the river.This allowed for long-term monitoring of the riverbank to observe the changes occurring during the study period.The discharges during the study period ranged between 420 m³ /sec to 2120 m³ /sec.The results showed that the amount of erosion on the riverbank was 62 cm, which can be considered slightly high given the current conditions in the region, and it should be taken into account that the rate may increase if favorable conditions arise for the erosion process.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".