Effect of soil loading rate on microbial activity during co-composting of diesel-contaminated clay soil
Bibliographic record
Abstract
The purpose of this experiment was to determine the effect of soil loading rate on the microbial performance during active phase co-composting of diesel-fuel contaminated clay soil under simulated windrow composting conditions. Microbial performance was monitored through relative heat generation, volatile solids destruction, and headspace oxygen/methane levels. Additional analyses in the form of radio-labelled diesel fuel, which was monitored through NaOH traps for respired 14CO2, and total petroleum hydrocarbon (TPH) concentrations were attempted during the experimental run. A total of seventeen biocells were used during the experiment. Soil loadings ranged from 0% contaminated soil to 30% contaminated soil. Each biocell received the same amount of compost amendments, with altering soil loadings. Biocells were placed in an environmental chamber for a duration of two weeks. During that time, the chamber temperature was ramped to simulate temperatures within a compost heap. Biocell height and temperature readings were taken at least three times daily. Air was supplied to the biocells for five minutes every hour, and the offgas from the biocells was bubbled through NaOH traps to capture respired CO2 and 14CO2. (Abstract shortened by UMI.)
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".