Reclamation of Hydrocarbon Contaminated Soils Using Soil Amendments and Native Plant Species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Petroleum hydrocarbons are among the top contaminants of the natural environment with serious concern worldwide due to their effects on soil, water, and surroundings. A two-year field experiment was implemented to evaluate reclamation of hydrocarbon contaminated (diesel fuel, crude oil) soils in central Alberta Canada using amendments (20% city waste compost, ammonium sulphate inorganic fertilizer) and seeding with a native grass mix. Soils amended with compost or compost-fertilizer had the greatest vegetation cover and biomass and lowest hydrocarbon concentrations at the end of the study. Fertilizer treatments had less vegetation cover and higher hydrocarbon concentrations, which were similar to the no amendment treatment. Seeding with native grasses had no effect on hydrocarbon degradation or total canopy cover, although vegetation composition showed some effect. Seeding increased cover of perennial native grasses in all amendment treatments, with greatest cover in compost and compost-fertilizer amended soils. Within two years after reclamation concentrations of F2 (carbon length > C10–C16) and F4 (>C34–C60) hydrocarbons in crude oil contaminated soils were below Canadian guidelines. Overall, compost was an effective amendment for reclamation of diesel fuel and crude oil contaminated soils and seeding was beneficial for reducing cover of non-native forbs. Fertilizer addition to compost may not enhance revegetation and remediation of hydrocarbon contaminated soils.
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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 it