Characterization of Waste Density in a Bioreactor Landfill via Microgravity and Settlement Analysis
Bibliographic record
Abstract
Abstract Optimizing the utilization of landfill airspace and production of biogas for use as an energy source relies on an understanding of the compaction, settlement, and stabilization of waste over time. A higher degree of compaction reduces the landfill footprint; however, it may not provide the optimal conditions for bacteria development and waste stabilization. This article reports on a research project piloting the use of repeated microgravity surveys to characterize variations in waste density in space and time in a bioreactor landfill. Over the duration of three years, four microgravity surveys were conducted on a new cell at a bioreactor landfill in Sainte-Sophie, Quebec, Canada, as it was gradually filled with waste up to a height of 25 m. After each survey, waste density values were extracted by comparing current gravity data with previous data from lower elevations. For accurate density computations, a settlement model was applied to estimate the degree to which waste had settled over time. This model was derived from settlement data recorded by two gauges buried in the waste column. The density values obtained from the microgravity surveys were 1.35 ± 0.31 g/cm3 in the first waste slab (between surveys 1 and 2), 1.44 ± 0.22 g/cm3 and 1.20 ± 0.25 g/cm3 in the second waste slab in the northeast and southwest ends of the cell, respectively (between surveys 1 and 3). In these cases, the microgravity and conventional air utilization factor (AUF) waste densities agreed within 3% when settlement gauges were within 90 m from the survey area. For the third waste slab (between surveys 1 and 4), the densities derived from microgravity decreased to 1.15 ± 0.28 g/cm3 and 1.12 ± 0.15 g/cm3 in the northeast and southwest ends of the cell, respectively. This decrease might be associated with water drainage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".