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Record W4415881875 · doi:10.58981/bluepapers.2025.2.01

The Ilulissat Icefjord: Local Stewardship and Global Responsibility in a Changing Climate

2025· article· en· W4415881875 on OpenAlexaboutno aff
Bo Albrechtsen

Bibliographic record

VenueBlue Papers · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsStewardship (theology)General partnershipCultural heritageClimate changePermafrostArcticLocal communityTraditional knowledgeCultural heritage management

Abstract

fetched live from OpenAlex

The Sermermiut archaeological site at the Ilulissat Icefjord contains cultural remnants from three Inuit cultures spanning nearly 4000 years. This unique site is now under threat from climate change and oceanic forces. The permafrost layer, which has long supported the site, including the cultural-historical ruins located on it, is thawing, causing destabilization of the ground and severe erosion of the slopes. A fieldwork initiative conducted in the summer of 2024 by collaborative teams from Greenland’s cultural and heritage institutions, in partnership with international technical assistance programs, studied these impacts using advanced monitoring techniques and community involvement, setting an example for adaptive management strategies that align with the UN 2030 Agenda. This article highlights how climate change is affecting both cultural heritage and contemporary life at the Ilulissat Icefjord, and emphasizes the importance of combining scientific research, responsible site management and local community engagement to safeguard this UNESCO World Heritage property. Through adaptive management, integration of local knowledge and strong collaboration across sectors, the Ilulissat Icefjord can remain both a globally significant natural site and a resilient, living Arctic community in a changing climate.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.007
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.255
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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