Bibliographic record
Abstract
Moreover, the report, titled'For the Benefit of Greenland' and authored 13 experts drawn mainly from the universities of Greenland and Copenhagen, cast grave doubts regarding the capacity of Prime Minister Aleqa Hammond's centre-left government coalition to implement reforms needed to maximise gains from the country's fledgling mining sector, despite Greenland's abundance of potentially commercial-sized rare earths, uranium and metal deposits. As for the opposition, Inuit Ataqatigiit (IA), Greenland's leftist-separatist party, and in particular, Sara Olsvig, an IA MP who holds one of Greenland's two seats in the Danish parliament (the Folketing) and who chairs the Danish parliament's Arctic Committee, said that one way to finance a diversified economy is by creating a profitable mining industry. [Minik Rossing] argued the island's confirmed mineral resource deposits fall short of providing a sustainable long-term basis to generate income for Greenland. The report calculated that in order for a Greenland economy to exist solely on mining, the island would need a minimum of 12 large-scale operating mines 2040. The report noted that just six commercial-scale mineral deposits have so far been identified. A mining-focused economic development strategy could also have serious negative consequences for Greenland's 57,000 inhabitants, said Rossing.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".