Frequency Analysis of Low Streamflow Characteristics Using Statistical Distributions
Bibliographic record
Abstract
The Deficit Below Threshold method was applied to characterize low flows in terms of volume, duration and intensity for 31 hydrometric stations in New Brunswick, Canada. The volume, duration and intensity data series were fitted to seven distributions which are the Pareto (PAR), gamma (GAM), Weibull (WEI), log-logistic (LLOG), log-normal (LN), Kappa (KAP) and exponential (EXP) distributions. The goodness of fit was assessed using the Anderson-Darling statistic. For volume, the KAP, LN, LLOG, PAR and WEI distributions gave the best fit. LN, LOG and KAP distributions were best fitted to the duration while the WEI, GAM, LLOG and the LN distributions provide the best fit for intensity. The skew coefficients (Cs) were also calculated for the data series for V, D and I and can provide insight to which distributions may be adequate to fit the low flow characteristics.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".