Laboratory‐Scale Measurement of Trace Gas Fluxes from Landfarm Soils
Bibliographic record
Abstract
Trace gas emissions from refinery and bioremediation landfarms were investigated in a mesocosm-scale simulator facility. Five simulators were constructed and integrated with a data acquisition system and trace gas analyzers, allowing automated real-time sampling and calculation of total hydrocarbon (THC), CO2, and water vapor fluxes. Experiments evaluating the influence of simulated cultivation and rainfall on trace gas fluxes from the soil surfaces were conducted. Results were compared with published field results. Results showed that cultivating dry or moderately wet soil resulted in brief enhancements of THC fluxes, up to a factor of 10, followed by a sharp decline. Cultivating dry soil did not enhance respiration. Cultivating wet soil did result in sustained elevated levels of respiration. Total hydrocarbon emissions were also briefly enhanced in wet soils, but to a lesser magnitude than in dry soil. Hydrocarbon fluxes from refinery landfarm soil were very low for the duration of the experiments. This lead to the conclusion that elevated THC fluxes would only be expected during waste application. An evaluation of the influence of simultaneous water vapor fluxes on other trace gas fluxes highlighted the importance in lab-scale experiments of correcting trace gas fluxes from soils. The results from this research can be used to guide management practices at landfarms and to provide data to aid in assessing the effect of landfarms.
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 | 0.000 | 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 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".