Restoring Industrial Disturbances with Native Hay in Mixedgrass Prairie in Alberta
Bibliographic record
Abstract
Native grassland restorations are often unsuccessful as a consequence of unreliable seed sources and competition from weeds and agronomic species. To improve on conventional approaches, we tested whether native hay can be used as a seed source for restoring native mixedgrass prairie on oil and gas well sites and associated disturbed areas. We also assessed the recovery of grassland that had been cut to harvest native hay. We assessed three wellsites seeded with native hay seven years earlier to evaluate longer term recovery, and they showed significant similarity to controls in adjacent grassland. Additionally, in 2012, five wellsites and an access road were seeded with native hay cut from grassland close to the sites. Grassland cut for native hay recovered within one year, showing similar species composition. In the second year, native hay restored wellsites had significant weedy annual cover; nevertheless, native grasses and forbs germinated, particularly needle grasses, wheatgrasses, and bluegrasses. Three of the native hay restored wellsites were sprayed with a non-selective herbicide in the third year; however, the remaining sites showed good recovery in the third year, with native grasses replacing most of the original weedy species. Collectively, results from this research suggest seeding from native hay is a successful and sustainable technique for restoring native vegetation cover and diversity on industrial disturbances in native grasslands.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".