Latin hypercube sampling for uncertainty analysis in multiphase modelling
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
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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