Parametric study on environmetal loads of hindcast and measured full scale data
Bibliographic record
Abstract
The reason of determining the probability of extreme occurrences of wind speed, wave height and current flow is due to the large impact these environmental loads have on the offshore facilities. These occurrences are difficult to predict and may happen once in many years. Several techniques can be used to extract and derive wind, wave and current information from the measured or measured full scale metocean data (PARAS). However, to rely on the measured metocean data alone may not be representative of the accurate sea conditions found locally. This requires correlating the measured metocean data to data derived from Hindcast. This research focuses on the parametric studies of two (2) types of data which are PARAS and hindcast. The hindcast model here is referred to as SEAMOS-South Fine Grid hindcast (SEAFINE) which is derived from a Joint Industry Project (JIP). The objectives of this research are to compare the statistical properties of measured full scale metocean data to the hindcast data through the correlation between winds, waves and currents of measured full scale metocean data and hindcast data in the basins off the coast of Malaysia and finally to validate with statistical certainty on the reliability of the hindcast data. This paper demonstrates some of the previous hindcast studies that have been done in some other regions. The statistical analysis, time series of metocean data and its correlation results are presented here in.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".