TL-moments and L-moments Estimation of the Generalized Logistic Distribution
Bibliographic record
Abstract
The generalized logistic (GLO) distribution has been used widely in extreme value event evaluation and also popularin hydrological risk analysis. In estimating the high return period events, censoring the data from below might be advantageoussince the small floods are less significant to large ones, so the used of small floods can sometimes be onlya nuisance value. In this paper the method of trimmed L-moments with one smallest value were trimmed (TLMOM1)was introduced as an alternative ways in estimating the flood for higher return period. TLMOM1 has an ability to reduceundesirable influence of small sample might have compared to former TL-moments (TLMOM) and L-moments (LMOM)method. The main objective of this study is to derive the TLMOM1 for GLO distribution. The performance of TLMOM1was compared with LMOM and TLMOM through Monte Carlo simulation and stream flows data over station in Terengganu,Malaysia. The result shows that in certain cases, TLMOM1 is a better option as compared to LMOM and TLMOMin modelling those series.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".