Substrate and Topsoil Impact on Soil Water and Soil Temperature in Arctic Diamond Mine Reclamation
Bibliographic record
Abstract
Soil properties in the Arctic are insufficiently explored and documented, particularly extensive monitoring of soil water and soil temperature over a period of time. Soil water and soil temperature are critical for understanding land surface and atmosphere interactions and are considered key factors for revegetation during mine reclamation. This study assessed how substrate and topsoil influenced soil temperature and soil water content at a reclaimed diamond mine in the Northwest Territories of Canada. Three substrates (crushed rock, processed kimberlite, and lake sediment) with and without topsoil were used. Mean air temperature changed little from year to year, although summer temperature showed a slightly increasing trend. Both annual and summer precipitation sharply declined over time. Soil water was influenced more by substrate than by placing 10 cm of topsoil on it. Processed kimberlite had greater water retention characteristics and water content than lake sediment and crushed rock substrates (significantly). Surface soil water content was lower with than without topsoil, suggesting that 10 cm of topsoil was not enough to influence it. Soil temperatures were not influenced by either substrate or topsoil. This study suggests processed kimberlite could be used as a substrate component for water and temperature management during reclamation of this extreme environment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".