Establishment of a Vegetation Cover on Tundra Kimberlite Mine Tailings: 2. A Field Study
Bibliographic record
Abstract
Abstract High erosion potential of dewatered kimberlite mine tailings after diamond extraction has prompted research at the Ekati Diamond Mine in the Canadian subarctic heath tundra ecosystem. Greenhouse and field studies aimed at establishing a permanent vegetation cover on these dewatered tailings began in spring 2000. Coarse texture, no organic component, lack of available macronutrients, and a serpentine chemistry are the principal limitations of kimberlite tailings to plant colonization. Structure‐improving (peat moss, lake sediment, and sewage sludge) and nutrient‐providing (fertilizer, rock phosphate, calcium carbonate, and gypsum) amendments were tested to ameliorate these conditions, facilitating the establishment of a permanent vegetation cover, which stabilizes surface materials and promotes natural colonization by the surrounding tundra vegetation. Seven native grass species (Arctagrostis latifolia, Calamagrostis canadensis, Poa glauca, Poa alpina, Deschampsia beringensis, Deschampsia caespitosa, and Festuca rubra) were used to measure amendment success. With the addition of structure‐improving and nutrient‐providing amendments, plant growth on these kimberlite tailings under field conditions was significantly improved over unamended tailings material. Tailings properties, including cation exchange capacity, organic carbon, and macronutrient availability, were also improved with amendment addition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".