Latin hypercube sampling for uncertainty analysis in multiphase modelling
Bibliographic record
Abstract
To facilitate the uncertainty analysis of a finite element multiphase multi-component transport model MOFAT, this paper provides guidance on latin hypercube sampling Monte Carlo (LHS-MC) sample size selection. To evaluate the ability of LHS-MC to produce output cumulative distribution functions (cdfs) that replicate random sampling Monte Carlo (RS-MC) cdfs, output cdfs obtained with LHS-MC sample sizes of 100, 300, and 500, and a RS-MC sample size of 10 000 are compared using the two sample Kolmogorov–Smirnov test. The LHS-MC cdfs for the three different sample sizes are able to accurately replicate the corresponding RS-MC cdfs for benzene, toluene, ethylbenzene, and xylene (BTEX) concentrations in the water, gas, and solid phases. The stability of LHS-MC is also evaluated by comparing three replicates of a LHS-MC sample. The three replicates are all able to accurately replicate the corresponding RS-MC cdfs for all BTEX concentrations in all three phases.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".