Quantifying hydraulic conductivity spatial variability for cement-based solidification/stabilization (S/S) remediation project: case study
Bibliographic record
Abstract
This paper presents statistical analyses of hydraulic conductivity data collected from an existing cement-based solidification/stabilization (S/S) system. The goal is to characterize the spatial variability of hydraulic conductivity and to examine sampling recommendations for the quality control (QC) program of that system to achieve target decision error probabilities regarding the acceptance or rejection of the system with respect to hydraulic conductivity. Over 2000 QC hydraulic conductivity samples, taken over an area of 300 000 m2, are used as a basis for these analyses. The hydraulic conductivity spatial variability is described by a marginal lognormal distribution with correlation function parameterized by directional correlation lengths, which are estimated by best fitting an exponentially decaying correlation model to sample correlation functions. The spatial variability associated with hydraulic conductivity of the studied S/S system is then utilized to assess sampling requirements for the QC program of that system. Considering the “worst case” correlation length and the hydraulic conductivity mean and variance, hypothesis test error probabilities are used to provide recommendations for conservative sampling requirements. It is believed that the analysis of this large construction project represents a unique opportunity to review the current practice of S/S field sampling requirements.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".