Utilization of Stream Power as a Scale to Detect the Deposition and Erosion Processes in Euphrates River, Iraq
Bibliographic record
Abstract
Stream Power is characterized as one of the principle forces driving the formation of river pattern.It's effectively used as an indicator to identify channel behavior in terms of its stability and potential for morphological modification.Therefore, the aim of this research is to examine and analyze the effect of Stream Power on instability and morphologically dominant processes within a part of the Euphrates River, and to assess the erosion and deposition processes using the Annual Geomorphic Energy Index (AGE).Several investigations were set up in ten river reaches to collect basic information for calculating parameters using principal hydraulic methods.These parameters include dominant and bankfull discharges, total and specific Stream Power (TSP and SSP) and the change in Annual Energy Balance (ΔAGE).Furthermore,to identify depositional and erosional processes, the results of AGE are compared with the Rapid Geomorphic Assessments (RGA) index, which includes indicators such as Channel Stability Indicators (CSI) and the Oklahoma Ozark Stream Bank Erosion Potential Index (OSEPI).The principle finding has clarified that the river is considered a low-energy stream and that stability is not viable.Also, the comparison of results shows that variations in discharge amounts are significant factors impacting the variance in energy balance and altering the depositional or erosional status; these factors are morphometric parameters (e.g., depth, width, slope of a reach, and channel formation).In addition, regions identified with active erosion phenomena coincide with lateral migration rates and landslides, which are closely linked with human activity, sediment texture, and riparian vegetation.The analysis have indicated that the areas most "sensitive" to variation are crucially subject to instability phenomena as a result of fluvial dynamics.Comparing the results of ΔAGE with the Rapid Geomorphic Assessments OSEP is more consistent with the depositional or erosional status.This confirms that the stability is not applicable for this low-energy stream and characterizing sediment prosesses conditions in terms of energy balance is an innovative technique that conceptually focuses on fluvial drivers.This work can provide useful indications to river dynamics processes and prepare a vision to the sensitive reaches conteroled by low-energy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".