Bibliographic record
Abstract
Greenland provides extensive and richly illustrated, area-specific knowledge about Greenland’s nature and landscape, history, culture, society and businesses as well as its towns and settlements. A total of 87 mainly Greenlandic researchers and experts have contributed with their knowledge to this book about the most essential topics from Cape Morris Jesup to Uummannarsuaq (Cape Farewell) and from Qaanaaq to Danmarkshavn. Covering almost 2.5 million km2 and with a population of around 56,000, Greenland is the world’s largest island and most sparsely populated area. Gain an insight into the history of geological formations and read about the ice sheet, which, as a result of melting and global warming, is now the subject of increased international interest. Understand the background of the marvelous icebergs at Qeqertarsuup Tunua and take the dog sled or snowmobile to the small remote and self-sufficient settlements. Take a tour of the capital of Nuuk and the other towns on the southern part of the west coast. This was where Danish-Norwegian missionary Hans Egede first set foot in 1721, and from where the Danish colonization began, which had a severe impact on Inuit culture. Read about modernization endeavors, the calls for secession, the influence of the rock band Sume and the referendums in 1979 and 2008 that led to Greenlandic self-government. Learn about the traditional culture featuring elements such as the drum dance, which has seen renewed interest and has been included on UNESCO’s World Heritage List of Intangible Cultural Heritage. Greenland covers all the essentials. From the most common to the most special.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.672 | 0.481 |
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".