Grazing and right-of-way affect native rangeland 12 years after pipeline construction in southern Alberta
Bibliographic record
Abstract
Over the past 100 years, large areas of native grasslands have been lost due to human activities and natural disturbances. Construction of pipelines for oil and gas transportation continues to pose significant challenges to grassland ecosystems. Thus, reclamation of disturbed native grasslands is critical for their existence in North America and around the world. This study investigated long-term (12 years) effects of grazing and right-of-way (RoW) treatments on revegetation of native rangeland on two pipelines in southeastern Alberta, Canada. Grazing and RoW treatments influenced soil and vegetation parameters; and plant species group responded differently at Milo and Porcupine Hills. Grazing was associated with significantly greater bare ground and decreased litter at both sites and increased vegetation cover at Porcupine Hills. At Milo grass density and biovolume increased as RoW disturbance increased, but not at Porcupine Hills. Trenching increased rhizomatous grasses and all RoW disturbances reduced tufted grasses. Vegetation was dissimilar on the RoW from undisturbed prairie with intermediate levels of disturbance (work, storage) having greater plant species diversity, whereas grazing had no effect. This study suggests 12 years may not be long enough for restoration of native rangelands after pipeline construction although there was some progress.
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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 | 0.001 | 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".