Methane removal efficiency and pore gas concentration data from a landfill soil column experiment under cyclic precipitation conditions
Bibliographic record
Abstract
This dataset presents results from a controlled laboratory soil column experiment designed to investigate methane (CH₄) consumption efficiency and gas concentration dynamics in landfill cover soil under imposed cyclic precipitation. The study was conducted between November 18 and December 20, 2024, at the Ecohydrology Research Group laboratory, University of Waterloo (Ontario, Canada), as part of a broader project on the Mitigation of Methane Emission Hot-Spots from Municipal Landfills. We include CH₄ removal efficiency (MRE) data and CH₄ and CO₂ fluxes measured from three soil columns over time, including inflow/outflow rates, gas concentrations in the headspace, and computed flux values. In addition, we provide depth-specific gas concentration profiles (CH₄, CO₂, O₂) within the soil columns across multiple timepoints and depths, supporting analysis of vertical gas gradients under variable saturation conditions. We analyzed gas samples using a GC-2014 Shimadzu Gas Chromatograph with a flame ionization detector and methanizer, and calculated CH₄ removal by comparing inflow and outflow fluxes. The dataset enables investigations into landfill soil biogeochemistry, gas transport processes, and the effects of precipitation cycling on methane oxidation potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".