Determining the Variability of the As-Placed Dry Density of Gap Fill Material
Bibliographic record
Abstract
Canada is one of the leading countries in nuclear energy usage for the past 60 years. Intermediate and high levels nuclear waste need to be contained and isolated in a deep geological repository, DGR, (i.e., 500 to 800 m below the ground level), using a multiple-barrier isolation system. Gap Fill Material (GFM) is a Bentonite based material that will be used to backfill the remaining spaces between the central bentonite blocks containing fuel waste containers and surrounding excavation walls. The Nuclear Waste Management Organization (NWMO) requires a simple and easy method to remotely determine the density of the GFM inside the DGR to ensure that it satisfies the density requirements, and consequently, limits the radiation of buried nuclear fuel into the surrounding environment. Several direct and indirect density measurement techniques were investigated, and a comprehensive laboratory testing program was performed. The program includes 277 Shear wave velocity and 59 Cone Penetration Tests on GFM samples with densities between 1.45 and 1.8 g/cm3. The results indicate strong relationships and provide some correlations between the GFM density, shear wave velocity, and cone penetration resistance. The laboratory results were then used to develop 5 artificial neural networks employing regression tool in MATLAB software with a high correlation coefficient (R-value) between the measured and predicted output variables.
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 | 0.000 | 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 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".