Cocomposting of Cattle Manure and Hydrocarbon Contaminated Flare Pit Soils
Bibliographic record
Abstract
The potential of using composting technology to remediate clayey soils with high levels of crude oil contamination was evaluated. An open air windrow comprised of flare pit soil, manure and wood chips was constructed at Olds College, Composting Technology Centre. Aeration and mixing were carried out by a skid steer loader and composting parameters were monitored for ten months. Temperature profile of this windrow gave cyclic patterns of high and low temperature recordings corresponded to the turning events. Most of the microbial metabolic activity occurred within the mesophilic temperature range and the hydrocarbon degrading microorganism populations remained high throughout the trial. Complete removal of BTEX compounds was achieved within six months and extractable carbons from C5 to C10 were reduced by 98.7% compared to the initial contaminated soil. Vegetative growth on the composted soil was also evaluated. Barley and timothy plants grown in the composted soil were compared to the control off-lease soil, contaminated soil, and other treatments of varying salinity and organic matter levels. Plant germination, survival, and biomass production was significantly better in the composted soil than in the contaminated soil. Furthermore, barley plants grown in the composted soil were more resilient than those grown in the control off-lease soil.
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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 | 0.000 |
| 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) | 0.001 | 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".