Bibliographic record
Abstract
Ground penetrating radar was investigated in an active landfill to determine if the in-situ water content could be measured. Water content is an important parameter in predicting the generation of landfill gas (LFG), an important renewable energy source. Unfortunately, predicting the quantity of LFG is difficult due to the heterogeneities present in a landfill and the lack of in-situ input parameters. GPR is a non-invasive, near-surface geophysical technique that provides high resolution images of dielectric properties in the earth's subsurface. A transmitter emits high frequency (10 - 1000 MHz) electromagnetic pulses through the subsurface, with the receiver recording the echo. Specialized software is then used to create images of the subsurface. The challenge with using GPR in landfills is the heterogeneity of the subsurface and the clay cap linear covering landfills, both affecting the transmission of the electromagnetic pulses. The use of GPR in a landfill was evaluated at the Region of Waterloo's Waste Management Centre. Measurements were completed using both the surface and the borehole approach. The results indicated that a borehole GPR can be used, with successful measurement of water content a function of borehole separation distance and frequency of the electromagnetic pulses. The developed approach was confirmed at the City of Hamilton's Glanbrook Landfill. The successful comparison of in-situ water content values to laboratory determined values at both landfills shows that GPR can be used to measure in-situ water content.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".