Utilization of Biosolids during the Phytoremediation of Hydrocarbon‐Contaminated Soil
Bibliographic record
Abstract
Addition of anaerobically digested sewage sludge (biosolids) to soil may improve conditions for phytoremediation of petroleum hydrocarbons (PHCs) through improved soil chemical, biological, and physical properties. A 32-wk greenhouse study investigated three rates of biosolids addition (0, 13.34, and 26.68 g oven-dry biosolids kg(-1) oven-dry soil) and the presence or absence of smooth brome (Bromus inermis Leyss. cv. Carlton) plants on the removal of diesel (3.5 g kg(-1) oven-dry soil) in an industrial, sandy loam soil. Diesel PHCs were divided into two fractions based on equivalent normal straight-chain boiling point ranges (F2: nC10-nC16; F3: nC16-nC34). The addition of biosolids did not increase the extent of PHC degradation but did result in significantly greater first-order decay constants compared to unamended controls. Overall, the presence of plants did not increase the rate or extent of PHC degradation, relative to that observed in unamended, non-vegetated soils. Vegetation was, however, an important factor within the biosolids-amended soils as was observed by a greater extent of PHC degradation. Some of this decrease was attributed to plant-induced removal of biosolids components that were contributing to the F3 fraction. Overall, the low-amendment rate (13.34 g oven-dry biosolids kg(-1) oven-dry soil) was considered to be the most effective treatment because it produced the greatest overall PHC degradation rate (0.226 wk(-1) for total PHCs) and resulted in the greatest recovery of biosolids-derived N by smooth brome (26.6%).
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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) | 0.000 | 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".