TECHNICAL NOTE: NITROGEN FERTILIZATION EFFECTS ON THE DEGRADATION OF AGED DIESEL OIL IN COMPOSTED DRILLING WASTES
Bibliographic record
Abstract
Hydrocarbon-contaminated wastes generated from oil and gas drilling activities may be used as a soil amendment once composted and further decomposition of residual hydrocarbons can be accomplished after the composts are applied to soils. To test if N fertilization may enhance hydrocarbon decomposition, we investigated the effects of N application on hydrocarbon degradation in different-aged composts (1-, 2-, 3-, and 4-year-old composts, coded as 1Y, 2Y, 3Y, and 4Y composts, respectively) through a pot experiment planted with white spruce (Picea glauca [Moench] Voss) seedlings. The percentage degradation of total petroleum hydrocarbon (TPH, C11 to C40) in the composts without N fertilization was correlated to initial NH4+ concentrations (R = 0.99, P < 0.001). The percentage degradation of TPH was highest in the 3Y compost (41.1%) that had an initial level of 325.3 mg NH4+ -N kg(-1) and the lowest in the IY compost (9.3%) that had an initial level of 8.3 mg NH4+ -N kg(-1). The degradation of TPH was enhanced by Nfertilization in the 1Y (from 9.3 to 15.3%) and 4Y composts (from 14.3 to 22.6%) that had low initial NH4+ concentrations. Our results show that application of NH4+ -based fertilizers may enhance the degradation of TPH when initial NH4+ concentrations in the compost are low.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".