Analysis of Erosion and Sediment Movement at the Confluence of Two Rivers (A Case Study of Tawaeli River, Central Sulawesi, Indonesia)
Bibliographic record
Abstract
The research was conducted at the confluence of two rivers on Tawaeli River, Nupabomba Village, Tanantovea District, Central Sulawesi, Indonesia. This research intends to determine the rate of erosion in the watershed as part of the source of sediment transport at the confluence of the Tawaeli River. The research method applied to collect primary data and secondary data. In determining the rate of erosion, researchers used the Universal Soil Loss Equation (USLE) method. By calculating the value of the erosion rate using the USLE method using ArcGIS, the total value of the erosion rate that occurs in the Upper Tawaeli watershed area is 13506,865 tons/year where each hectare of the watershed is able to produce sediment from the erosion process of 4,3977 tons/ha/year included in the very low classification based on the criteria for the value of the erosion hazard level, and based on the erosion hazard level class, most of the watershed area, which is an area of 3053,572 ha or equivalent to 99.42% of the total area of the Upper Tawaeli watershed, is included in the area category with a very low level of erosion hazard. The results of the analysis through the sediment delivery ratio (SDR) using the Auserwald equation obtained an SDR value of 0.223 which indicates that not all sediment from the erosion process is transported or enters the river, therefore the sediment produced at the river outlet (STA. 3.4). is smaller than the erosion rate result of 261,278 tons/year. In addition, the results of this research also reveals that the configuration of the riverbed at the meeting point of the tributary has changed from the previous one which had a base elevation of 175,143 m, after an aggradation phenomenon occurred which was marked by an increase in the elevation of the riverbed as high as 0.348 meters.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".