Morphological Study of The Liliba River Utilizing Remote Sensing System
Bibliographic record
Abstract
The branch of natural sciences called river morphology focuses on the study of the characteristics and dynamics of rivers, including their structure, classification, and changes on spatial and temporal scales. Two main factors influence river configurations. The first is natural factors, such as floods and landslides, and the second is human factors, such as human activities that alter river morphology. Cyclone Seroja caused landslides on the banks of the Liliba River in Kupang, East Nusa Tenggara in April 2022. This mainly occurred at Naimata Bridge in Liliba Village, Oebobo District. River geometry, especially the channel and bed elevation, can be significantly influenced by landslides occurring on the riverbanks. Therefore, a study of the morphology of the Liliba River was conducted using remote sensing systems. To conduct a detailed analysis, this study incorporated these photos. In this review, changes in the river channel were examined by extracting the river's course from Landsat image data. From 2009 to 2022, the Liliba River experienced an average shift of 1.60 meters westward and eastward. The research results indicate the need for reforestation along the riverbanks, and residents should be encouraged to reduce the disposal of plastic waste into the river. Future research should also further investigate the geological characteristics of the Liliba River, such as rock types, and conduct hydrological analyses to comprehensively understand the factors influencing changes in the riverbed.
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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 | 0.003 | 0.002 |
| 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".