Hexadecane mineralization and denitrification in two diesel fuel-contaminated soils
Bibliographic record
Abstract
The effect of nitrate, ammonium and urea on the mineralization of [14C]hexadecane (C16H34) and on denitrification was evaluated in two soils contaminated with diesel fuel. In soil A, addition of N fertilizers did not stimulate or inhibit background hexadecane mineralization (4.3 mg C16H34 kg−1 day−1). In soil B, only NaNO3 stimulated hexadecane mineralization (0.91 mg C16H34 kg−1 day−1) compared to soil not supplemented with any nitrogen nutrient (0.17 mg C16H34 kg−1 day−1). Hexadecane mineralization was not stimulated in this soil by NH4NO3 (0.13 mg C16H34 kg−1 day−1), but the addition of NH4Cl or urea suppressed hexadecane mineralization (0.015 mg C16H34 kg−1 day−1). Addition of 2 kPa C2H2 did not inhibit the mineralization process in either soil. Denitrification occurred in both soils studied when supplemented with NaNO3 and NH4NO3, but was not detected with other N sources. Denitrification started after a longer lag in soil A (10 days) than in soil B (4 days). In soil A microcosms supplemented with NaNO3 or NH4NO3, rates of denitrification were 20.6 and 13.6 mg NO3− kg−1 day−1, respectively, and in soil B, they were 18.5 and 12.5 mg NO3− kg−1 day−1, respectively. We conclude that denitrification may lead to a substantial loss of nitrate, making it unavailable to the mineralizing bacterial population. Nitrous oxide was an important end-product accounting for 30–100% of total denitrification. These results indicate the need for preliminary treatability studies before implementing full-scale treatment processes incorporating commercial fertilizers.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".