Application to Use SARAL altimetry for water level monitoring over Ramganga reserviour and its correlation with MODIS data
Bibliographic record
Abstract
Retrieval of sea surface height by performing altimetry on SARAL datasets over Ramganga reservoir is presented in this paper. Satellite altimetry for inland water bodies has evolved from investigation of water height retrieval to monitoring over a past few decades. The purpose of performing altimetry over inland water bodies is to estimate the water level and deal with problems such as flood and reservoir operations. Firstly we located the geographical location of our study region and marked all the track numbers passing over it. Then SARAL datasets specific to the marked track numbers were collected to carry out processing on them. A methodology was followed to process the datasets in BRAT for the sea surface height retrieval over the Ramganga Reservoir which fulfilled all the necessary requirements. Retracking was also applied for precise results. The results were then correlated with NDWI values of MODIS data. SARAL derived sea surface height information for ten different months of the same year was correlated with NDWI information to obtain a relation in the form of a quadratic equation which was used for validating data. The result supports that SARAL AltiKa dataset can be utilized for more accurate water level information over inland water bodies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".