<i>In situ</i> Moisture Content Measurement in MSW Landfills with TDR
Bibliographic record
Abstract
Moisture content has an important effect on biodegradation rates in landfills. In situ moisture measurement is, therefore, at the center of any scientific studies related to optimal operation of bioreactor landfills. Because of the material heterogeneity, there is no commonly accepted way for in situ moisture measurement in wastes. The goal of this paper is to develop the instrumentation and analytical procedures to measure in situ moisture content in MSW materials. The system is based on Time Domain Reflectrometry (TDR), which had to be improved for moisture measurement in wastes. In particular, TDR probes have to be calibrated for the specific materials, and the effect of varying leachate electrical conductivity has to be reduced. A series of experiments were conducted with different waste materials and mixtures. The materials and the liquid electrical conductivity (eC) were varied systematically. The results show that a fourth-degree polynomial calibration equation, albeit with slightly differing coefficients for different materials, provides excellent fit (r2 values over 0.99). Further, the type of material can be substituted by the porosity of the material to select the appropriate calibration coefficients. The variation of leachate electrical conductivity was eliminated at high eC values and noticeably reduced at low eCs (0.03-0.95S/m) by coating the TDR probes. These results indicate that TDR is a viable instrument to measure the in situ moisture content in landfill.
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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 | 0.001 |
| 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) | 0.001 | 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".