Bibliographic record
Abstract
Abstract Greenland has a long mining and mineral exploration history and offers interesting possibilities for investors. There is still optimism in the mineral business, but successful examples are surprisingly few in the new millennium. Based on numerous new tables compiling information on companies, periods, targets, licenses, and costs, this paper gives a description of the past and present activities, the exploration companies involved, their main targets, their limited financial power, and their continued need for and search of investors and large industrial partners. An analysis of the key drivers at different levels is presented: analogues with Canada and elsewhere, dedicated prospectors looking for profit, specific strategic projects, commodity prices, new research results, co-financing, strategies, and regulations by authorities in Greenland and Denmark. Changes in political agenda in Greenland, Denmark, and internationally have had a strong influence on exploration activities in Greenland compared to other countries with an exploration industry, in some cases creating good incentives for investors, in other cases being showstoppers for future exploration and mining. This paper provides, for the first time ever, a summary of the total costs for mineral exploration in Greenland and the total revenue for the governments, and compares these numbers with the public investments in research, data acquisition, and direct investments in national companies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".