Native and Dryland Pasture Seed Mixes Impact Revegetation 12 Years after Pipeline Construction in Southern Alberta
Bibliographic record
Abstract
Activities associated with agriculture, grazing, and the energy industry have altered large tracts of native rangeland in North America. Pipelining causes intense local disturbance by removal of vegetation and alterations to soil horizons. Following a disturbance, reclamation is required to return the land to equivalent land capability. Revegetation is usually by seeding native and/or agronomic (non-native, dominant) species. This study investigated the long-term effects of native and dryland pasture (91% non-native species) seed mixes, grazing, and right-of-way (RoW) treatments on revegetation of native rangeland in southeastern Alberta. Native seed mixes were more successful at enhancing seeded vegetation cover than dryland pasture seed mixes. Grazing had a significant impact only on the survival of non-native grasses. The seed mix did not significantly affect total, native, non-native, annual, or perennial forb cover. Total forb cover was significantly higher on the trench with the dryland pasture seed mix than all other RoW treatments (storage, work). This long-term study suggests that native seed mixes can result in successful revegetation of reclamation following pipeline construction.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".