Assisted dispersal and retention of lichen‐dominated biocrust material for arctic restoration
Bibliographic record
Abstract
Arctic biocrust loss due to industrial activity can have long‐lasting ecological impacts, highlighting the importance of restoring disturbed tundra environments. This research focused on biocrust establishment on substrates of by‐product materials from diamond mining (crushed rock, lake sediment, processed kimberlite), inoculant dispersal (dry placement, slurry), habitat amelioration (erosion control blanket, tundra soil, woody debris), and containment (jute mat), over three field seasons at Diavik Diamond Mine, Inc., Northwest Territories. Three years after inoculation, lichens were detected on 100% of inoculated plots and 70% of uninoculated plots (likely blown in from inoculated plots). Uninoculated plots had significantly lower species richness and vegetation cover than inoculated plots. Biocrust retention was highest on plots with erosion control blanket, containment, woody debris, and crushed rock; larger scale application of these treatments should be assessed in future. Plots with processed kimberlite, no habitat amelioration or tundra soil, and no containment had the lowest cover, species richness, and individual species abundance in year 3. This research suggests that active restoration techniques using lichen biocrust inoculation and habitat amelioration is required for successful biocrust revegetation outcomes on substrates of mining by‐products in the arctic.
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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 | 0.001 |
| 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".