Multivariate Technique for Validating Historical Hydrometric Data with Redundant Measurements
Bibliographic record
Abstract
The aim of this research was to develop an automated methodology for validating chronological series of natural inflows to reservoirs. Theoretically, gauges located on the same reservoir should indicate the same reading. However, under the influence of meteorological and hydraulic factors, or simply because of failed measuring equipment, there may be large deviations between the various measurements. Since the calculation of historical natural inflows is directly linked to the measurement of reservoir level by the water balance equation, there will be as many series of natural inflows as there are of reservoir levels. A multivariate filtering technique is used to validate the historical natural inflow computed by each water level variation. The multi filter methodology has the advantage of balancing the water volume of natural inflows to the reservoir when applied over a relatively long period of time. As a result, the validated flood peaks are not systematically overestimated or underestimated and the validated natural inflows are nearly identical for all the gauges. The proposed technique has been incorporated into a software program called ValiDeb, which has been successfully tested on-site on the Gatineau River in Quebec.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".