Composting of soils contaminated with heavy petroleum hydrocarbons.
Bibliographic record
Abstract
This project tested at the field scale, five on-site, non-proprietary bioremediation processes on weathered petroleum hydrocarbons from a fire fighter training area. Two bioremediation processes based on fungi (commercially produced white rot fungus, Pleurotus ostreatus, and aged, coarse wood chips, 'compost', with naturally occurring fungi) were applied with variations and compared to one control: a typical static biopile. An elevated-face compost turner was used to turn the soil in selected windrows for aeration. Statistically-based sampling was employed and quality control measures were enforced for sampling and analysis. The treatment options examined for the contaminated soil were: (1) white rot fungus and compost, (2) compost, poultry manure and turning, (3) compost, synthetic nitrogen-phosphorous-potassium fertilizer, and turning, (4) compost, the above synthetic fertilizer, and no turning, and (5) the above synthetic fertilizer, and no turning (static biopile). The compost and poultry manure process performed the best, remediating 35 tonnes of soil contaminated with 6000 mg/kg of mineral oil and grease (MOG) to the remediation criteria of 1000 mg/kg in 19 days and to less than 300 mg/kg in less than 56 days. The net rate of bioremediation was 100 mg/kg/day of MOG. The estimated cost of this process for commercial applications, excluding labour, excavation and site preparation, was $18 to $29 per tonne, depending on the cost of the poultry manure.
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 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 | 0.000 | 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 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".