Natural Flow Reconstruction Using Kalman Filter and Water Balance–Based Methods I: Theory
Bibliographic record
Abstract
Because natural flow (NF) values are either not directly measured or have the potential to contain considerable error, when deemed necessary, the reconstruction of a reliable NF series is ostensibly important. Selecting the appropriate method depends on available data. For a time period before reservoir construction (pre-reservoir construction period), the only available data for ungauged basins came from the neighboring basins and simulated flow used in a rainfall-runoff model. A new Kalman-based method developed in this paper looks to reconstruct the NF series using the state fusion technique, which is then compared with the area ratio method, the maintenance of variance (Move) type III method, and the multivariable regression method using different quality indexes (QIs). In the perspective of the post-reservoir construction period, when hydrometric data (i.e., turbine flow, water level in the reservoir, and discharged flow) is collected in an ungauged basin (with no flow measurements), a new water balance equation (WBE)-based method is recommended for reconstructing and filtering the NF data using an optimization technique that would then be compared with the classic WBE that implements different QIs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".