TL-moments and L-moments Estimation of the Generalized Logistic Distribution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it