Vulnerability assessment of selected key sites in Aasivissuit – Nipisat UNESCO World Heritage Area, West Greenland. Nipisat, Arajutsisut, Innap nuua & Itinnerup Tupersuai
Bibliographic record
Abstract
Harmsen, H., Hagen, D. & Buschman, V.Q. 2022. Vulnerability assessment of selected key sites in Aasivissuit – Nipisat UNESCO World Heritage Area, West Greenland. Nipisat, Arajutsisut, Innap nuua & Itinnerup Tupersuai. NINA Report 2168. Norwegian Institute for Nature Research.\nThis report details an assessment of vulnerability of the four key sites of Nipisat, Arajutsisut, Innap nuua and Itinnerup Tupersuai in West Greenland’s UNESCO Heritage Area, Aasivissuit – Nipisat, Inuit Hunting Ground Between Ice and Sea (inscribed 2018). The work was performed in August 2021 by researchers from the Greenland National Museum and Archives (NKA), Norwegian Institute for Nature Research (NINA), Greenland Institute of Natural Resources (GINR) and National Museum of Denmark. This current study is part of a broader effort to develop a suite of baseline data for identifying current ‘weak spots’ in the terrain and provide metrics by which changes to the cultural heritage, wildlife, and vegetation can be measured in the coming years. The data produced from this report will also facilitate the future drafting of Site-Specific Guidelines at these localities by informing tourists, cruise operators, and community members of the location of protected ancient cultural remains, vulnerable vegetation and sensitive wildlife in the area. This assessment serves as a prerequisite for ensuring Aasivissuit - Nipisat remains a unique and sustainable cultural landscape and that the area’s Outstanding Universal Values (OUV) are protected for the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".