Diavik Waste Rock Project: Geostatistical Analysis of Sulfur, Carbon, and Hydraulic Conductivity Distribution in a Large-Scale Experimental Waste Rock Pile
Bibliographic record
Abstract
One of the large-scale field waste rock experiments (test piles) conducted as part of the Diavik Waste Rock Project was deconstructed, providing a spatially located set of geochemical, mineralogical, and particle-size distribution samples. Geostatistical analyses were conducted for sulfur and carbon content and saturated hydraulic conductivity, which affect the geochemical evolution of waste rock, to investigate the spatial dependence of these parameters. Analyses included population statistics, experimental semi-variogram estimation, and theoretical semi-variogram fitting. Population statistics were calculated for additional data sets from samples collected during the construction of the test piles. The population statistical analyses indicated that log-normal distribution provided the best fit for all investigated data sets. Experimental semi-variograms were estimated for the spatially located data set (test pile deconstruction) using the classical estimator, and theoretical semi-variograms were fitted. This investigation showed that the spatial distribution of sulfur, carbon, and hydraulic conductivity within the core of the test-pile experiments can be approximated using a log-normal distribution with a mean and standard deviation calculated using the samples collected during construction of the piles, and that little to no spatial relationship was present for these parameters at the scale of sampling. That the saturated hydraulic conductivity of the matrix material can be represented by the same statistical distribution throughout the test pile is significant because water flow, as well as mineral surface area and reactivity are dominantly controlled within the matrix portion of the test pile. Reactive transport simulations are included to demonstrate the influence of the matrix material on effluent geochemistry.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".