Characterization of Solid Waste in a Bioreactor Landfill Using Seismic Borehole Methods
Bibliographic record
Abstract
The biodegradation of solid waste in bioreactor landfills is dependant on a number of factors including moisture content. Identification of relatively wet and dry areas throughout the landfill would help operators determine where moisture should be added or not. This study attempts to utilize seismic borehole techniques to identify changes in seismic wave velocity through the waste due to the addition of moisture. The study is based on the principle that P-wave velocity in a porous material increases with moisture content. The bioreactor landfill surveyed is located in Ste. Sophie, Quebec, Canada. Two vertical boreholes were installed 10 m apart and to a depth of 15 m, in order for vertical seismic profiles to be obtained. An initial characterization survey was conducted to understand seismic wave propagation through the waste. The P-wave velocity is on the order 290–370 m/s and the S-wave velocity is on the order of 140–175 m/s. Two-day surveys were conducted to note changes in P-wave velocity under relatively dry and wet conditions. There was no measurable change in P-wave velocity after the addition of approximately 7000 L of water between the boreholes. This is most likely due to that fact that it was not possible to sample the area between boreholes where change was most likely to occur and it was not possible to control exactly where the water was being added.
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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 | 0.000 | 0.000 |
| 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".