Natural revegetation of hydrocarbon-contaminated soil in semi-arid grasslands
Bibliographic record
Abstract
One way to identify hydrocarbon-tolerant plant species for reclamation is to sample vegetation at contaminated sites allowed to recover naturally. We compared vegetation and soils of 14 hydrocarbon-contaminated plots in southern Saskatchewan to those of nearby uncontaminated plots to determine the impact on plant communities and soil properties. Contaminated plots had less vegetation and litter cover than uncontaminated plots, and significantly higher soil carbon to nitrogen ratios, pH, and hydrocarbon concentration, and lower nitrogen and phosphorus. Although species richness was not significantly different, Shannon's diversity was lower on contaminated plots. Mean compositional similarity of the plots, measured using Jaccard's index, was only 31%, and cover similarity, measured using Spatz's index, was only 22%. Vegetation composition differences occurred because mycorrhizal, woody and vegetatively reproducing species, and species using birds or unassisted means for seed dispersal were significantly less common on contaminated than uncontaminated plots. Self-pollinated species were significantly more common on contaminated plots. The most abundant species on contaminated soils were the annual forb Kochia scoparia and the native perennial grasses Hordeum jubatum, Distichlis stricta, Agropyron smithii, Agropyron trachycaulum, and Poa canbyi. This research shows that some plant species and functional groups are tolerant of the altered soil conditions at hydrocarbon-contaminated sites.Key words: functional groups, oil spills, phytoremediation, reclamation, succession, vegetation recovery.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".