Statistical sample size for quality control programs of cement-based “solidification/stabilization”
Bibliographic record
Abstract
Sampling requirements for the quality control (QC) of cement-based “solidification/stabilization” (S/S) construction cells do not currently specify the sample size considering the accuracy of the estimated effective hydraulic conductivity of the cells from the samples, nor considering the risk associated with drawing the wrong conclusions about the acceptability of the cells. In this paper, probabilistic simulations are performed to examine the influence of a soil–cement material’s mean, variance, and correlation length on sampling requirements for a QC program of cement-based S/S construction cells. The sampling requirements are determined by considering a hypothesis test, having nulled that the constructed material is unacceptable, and targeting acceptable probabilities of making an erroneous decision. Two types of errors can be made: (i) concluding that the material is acceptable when it actually is not, or (ii) failing to conclude that the material is acceptable when it actually is. The paper investigates how many samples are required to keep the probabilities of making these errors acceptably small. Plots are provided, which can be used to estimate the required number of samples. The paper concludes by discussing how the simulation-based results compare with current sampling requirements for the QC of an actual set of cement-based S/S construction cells.
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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 | 0.036 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".