Barren Lands: An Epic Search for Diamonds in the North American Arctic
Why this work is in the frame
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Bibliographic record
Abstract
Book Review| March 01, 2002 Barren Lands: An Epic Search for Diamonds in the North American Arctic Kevin Krajick; Kevin Krajick 1Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401 Search for other works by this author on: GSW Google Scholar Paul M. Santi Paul M. Santi 1Department of Geology and Geological Engineering, Colorado School of Mines, Golden, CO 80401 Search for other works by this author on: GSW Google Scholar Environmental and Engineering Geoscience (2002) 8 (3): 243. https://doi.org/10.2113/8.3.243 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Kevin Krajick, Paul M. Santi; Barren Lands: An Epic Search for Diamonds in the North American Arctic. Environmental and Engineering Geoscience 2002;; 8 (3): 243. doi: https://doi.org/10.2113/8.3.243 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentBy SocietyEnvironmental and Engineering Geoscience Search Advanced Search The reader who opens Barren Lands should be prepared to enter a world of cold, ice, gravel, and misery populated by fanatically devoted workaholic geologists with disagreeable personalities. You will be captivated. You will probably also be driven to finish the book so that your life can return to normal, because the harsh images painted by Kevin Krajick will pop into your consciousness throughout the day. The book chronicles the exploration leading to the opening of the Ekati Diamond Mine in the Northwest Territories of Canada in 1997. Although the author divides the text into four parts that flow in... You do not currently have access to this article.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it