Applicability of Geospatial Tools for Long-Term Sediment Deposit Analysis and Identification of Paleo-Channels. A Case Study of Ganga River Basin. Bihar, India
Bibliographic record
Abstract
The river Ganga and its tributaries are important water resources for North India. It has its own ecosystem and also creates a unique biodiversity around its vicinity. It has been found that huge sediment deposits take place in the River Ganga basin from past few decades. The flow area of many rivers is reducing continuously. During the flood, it inundates the large agriculture and urban areas. It caused a huge economic loss along with human death & displacement. The organisation involved in the flood management and related works are facing the difficulties in long term planning and development of mitigation strategies such as river dragging, river training works, flood & disasters management etc. The evidence of sediment deposition is highly required in the public domain to use in overall project development for this basin. Therefore, a study was conducted to estimate the sediment deposit rate and its areas of the basin. Various scientific methods, such as satellite imagery interpretation, digitization, modelling techniques have been used. The result obtained by the study indicates the continuous rise in sediment deposition in the river basin. The detail of the sediment deposit rate, area and locations of the river Gandak, Budhi Gandak, Bagmati, Kamla Balan, Kosi and Mahananda along with the methodology are provided in this manuscript. The methodology developed in the study are tested and reliable for the application on another river basin too.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".