Biodegradation of Petroleum Hydrocarbons in a Tropical Ultisol Using Legume Plants and Organic Manure
Bibliographic record
Abstract
Global emphasis on food security and soil health should consider rehabilitation of degraded lands, especially where oil contamination limits the use of such lands. Three legume plants (Gliricidia sepium, Leucaena leucocephala and Calapoconium caerulean) alone or along with 0.5% (w/w) (equivalent of 10 tons ha-1) poultry manure, were used to treat the soil, in which the oil residuals were monitored for three years. Results showed that significantly high levels of residual total petroleum hydrocarbon content (RTPHs) persisted in the non-amended soil after 36 months. At 3 months, 43% of RTPHs was removed by Gliricidia sepium and Leucaena luecocephala along with poultry manure. Net loss of RTPHs after 12 months was 69% for Gliricidia sepium and Leucaena luecocephala combined with poultry manure and only 38% for A5, explaining that, degradation of petroleum hydrocarbon by indigenous soil micro-organisms was very low. At 18 months when additional load of oil was applied, the degradation rate increased from 71.7 mg kg-1 day-1 to 142 mg kg day-1 within 6 months for all the legume plants along with poultry manure. This explained increased in number of hydrocarbon-degrading micro-organisms due to increase in oil load. The oil inhibited germination and yield of maize crop completely. Treatments with legume plant along with poultry manure significantly (p < 0.05) increased both germination and yield of maize crop. The effects of legume plants combined with poultry manure were the better treatment than legume or poultry manure alone in improving the soil properties for growth and performance of maize crop.
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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".