Recovery of Tundra Vegetation Three Decades after Hydrocarbon Drilling with and without Seeding of Non-Native Grasses
Bibliographic record
Abstract
ABSTRACT. Oil and gas exploration conducted in the 1970s left a legacy of abandoned test wells with sumps, containing drilling waste, in the Mackenzie Delta region of Canada’s Northwest Territories. One to two years after the test wells were decommissioned, a set of sites were seeded with non-native grasses and fertilized to test whether these treatments could accelerate vegetation recovery and prevent erosion. We sampled seeded and unseeded sumps and adjacent tundra vegetation in the Mackenzie Delta region three decades later to examine the impact of post-disturbance seeding treatments on site recovery. Plant species composition and environmental data were collected at 12 sump sites (6 seeded and fertilized and 6 unseeded and unfertilized) in lowland and upland tundra. Multivariate analyses using NMDS and perMANOVA indicated that in the lowlands, seeding and fertilization treatments had small but significant effects on plant species composition that differentiated seeded from unseeded sump caps. Plant communities on sump caps for all treatment types were significantly different from surrounding undisturbed tundra, even after more than 30 years of recovery. Seeded non-native grasses were found on both seeded and unseeded sumps, but not in the surrounding undisturbed tundra. Undisturbed tundra appears resistant to the spread of introduced agronomic grasses, but disturbed areas, such as sumps, provide areas of suitable habitat where non-native plants can persist. Key words: revegetation treatments; low Arctic tundra; plant invasion; oil and gas exploration; Kendall Island Bird Sanctuary; long-term monitoring
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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 | 0.000 | 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) | 0.001 | 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".