A Statistical Model for Landfill Surface Emissions
Bibliographic record
Abstract
Landfill operators require a rapid, simple, low-cost, and accurate method for estimation of landfill methane surface emissions over time. Several methods have been developed to obtain instantaneous field measurements of landfill methane surface emissions. This paper provides a methodology for interpolating instantaneous measurements over time, taking variations in meteorological conditions into account. The goal of this study was to determine the effects of three factors on landfill methane surface emissions: air temperature, pressure gradient between waste and atmosphere, and soil moisture content of the cover material. On the basis of a statistical three-factor and two-level full factorial design, field measurements of methane emissions were conducted at the City of Montreal landfill site during the summer of 2004. Three areas were measured: test area 1 (4800 m2), test area 2 (1400 m2), and test area 3 (1000 m2). Analyses of variance were performed on the data. They showed a significant statistical effect of the three factors and the interaction between temperature and soil moisture content on methane emissions. Analysis also led to the development of a multifactor correlation, which can be explained by the underlying processes of diffusive and advective flow and biological oxidation. This correlation was used to estimate total emissions of the three test areas for July and August 2004. The approach was validated using a second dataset for another area adjacent to the landfill.
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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 | 0.001 | 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".