The Use of the Iñupiaq Technique of Tundra Sodding to Rehabilitate Wetlands in Northern Alaska + Supplementary Appendix Tables (See Article Tools)
Bibliographic record
Abstract
Tundra sodding, a new technique available to rehabilitate disturbed wetlands in the Arctic, is based on Iñupiaq traditional knowledge. C. Hopson, an Iñupiaq elder from Barrow and author of this paper, guided the development and field application of this new technique by providing traditional knowledge he learned as a youth from his elders. Tundra sodding has several advantages over other land rehabilitation techniques, the most important being that it can establish a mature plant community of indigenous species in a single growing season. In all sampling years, the plant communities at sodded sites were dominated by two rhizomatous graminoids, Eriophorum angustifolium and Carex aquatilis. These sedges also were dominant in all years in reference tundra. Also common to the plant communities in both reference tundra and sodded sites were 18 other vascular species (grasses, evergreen and deciduous shrubs, and forbs). Results from two to five growing seasons indicate that tundra sod can reduce the overall subsidence due to thawing of shallow permafrost. We harvested sod on three occasions from an area slated for gravel mining. In the summers of 2007 and 2008, we transplanted 334 m2 of tundra sod to portions of three sites to test the feasibility of the method. In summer 2010, we used the experience gained from that work to rehabilitate an entire site (1114 m2). This tundra sodding technique is labor intensive and costly compared to other rehabilitation techniques, but it offers advantages that justify its use when rapid rehabilitation of a disturbed site is needed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.020 | 0.001 |
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".