Evaluation of spring flows using recession flow analysis techniques
Bibliographic record
Abstract
ABSTRACT This study delves into the analysis of recession characteristics of spring base flow, focusing on the Hill campus spring (elevation of approximately 2,150 m) and Fakua spring (elevation of approximately 1,850 m) in the Tehri Garhwal district of Uttarakhand, India. Spanning from January 1999 to December 2004, discharge data from G.B. Pant University of Agriculture and Technology's Hill campus are employed. The research employs an automated, objective-based method to generate master recession curves (MRCs) and categorize them into early, intermittent, and late recession segments. Statistical parameters and low-flow indices from flow duration curves (FDCs) are utilized for flow assessment, while base flow indices (BFIs) are analyzed using the Web-based Hydrograph Analysis Tool (WHAT). The findings highlight the importance of water storage strategies during the rainy season for the sustainable utilization of spring water. The study suggests that the techniques applied are equally applicable for analyzing river flow recession characteristics, emphasizing the broader implications for integrated water resources planning and management in mountainous regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".