Frequency analysis of annual maximum suspended sediment concentrations in Abiod wadi, Biskra (Algeria)
Bibliographic record
Abstract
Abstract Sediment transport processes in the Mediterranean semi‐arid areas have interested a great number of hydrologists and statisticians. Frequency analysis (FA) procedures are commonly applied for several hydrological events such as floods and droughts. However, in general, FA is not widely applied to treat suspended sediment concentration (SSC), especially in semi‐arid regions. In the present study, an FA was performed on SSC data from 1979 to 1991 observed upstream from the Foum El Gherza dam, which is located at Biskra in the South–East of Algeria. This dam is problematic in terms of silting. Therefore, this study is mainly motivated by providing an FA model and risk evaluation in order to assist dam managers to better evaluate the potential of silting resulting from SSC transport to the reservoir. Probability distributions commonly used in hydrology were tested to SSC data recorded at the M'chounech station on Abiod wadi located upstream the dam. All the FA steps were considered; including classical techniques (e.g. goodness‐of‐fit tests, statistical criteria) as well as recently developed tools (tail distribution classification). Their application led to the selection of the lognormal distribution (LN2) to fit the considered data, and hence an accurate risk assessment could be obtained. Because of the presence of a slight trend, non‐stationary models are also considered in the present study. Copyright © 2013 John Wiley & Sons, Ltd.
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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".