Arctic ecosystem restoration with native tundra bryophytes
Bibliographic record
Abstract
Bryophytes are ecologically essential to northern ecosystem restoration after disturbance. In this study, native bryophytes were used to revegetate two Arctic restoration sites. Different propagation types (small, medium, large fragments) and substrates (crushed rock, lake sediment, processed kimberlite in Canada; volcanic silt loam and crushed lava rock in Iceland) with two erosion control treatments (with and without cheesecloth) were evaluated. After two growing seasons, large bryophyte fragments resulted in the greatest density and total and live cover with erosion control and medium fragments resulted in the highest density and species occurrence without erosion control. Erosion control significantly increased live cover, total cover, species occurrence, and density, including a tempering effect on soil volumetric water content and temperature. Substrates with more heterogeneous surfaces (crushed rock, volcanic silt loam, crushed lava rock) yielded higher live cover, density, and spontaneous colonization than more homogeneous substrates (processed kimberlite, lake sediment) and can be more suitable for use in arctic ecosystems revegetation. The positive outcomes in both Canada and Iceland led to the conclusion that bryophyte propagation with large to medium fragments, erosion control with cheesecloth, and substrates with heterogeneous surfaces would be effective restoration approaches where bryophyte revegetation is a focus.
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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".