Bibliographic record
Abstract
More than a century ago, a prospector discovered gold at Ontario’s Kirkland Lake and a son was born to British immigrants in Saskatchewan. The boy – Norman Bell Keevil – went on to become a renowned scientist, teacher, and prospector, discovering a small but high-grade copper mine in Ontario. Parlaying that into control of the Kirkland Lake gold mine fifty years later, he formed the fledgling mining company Teck Corporation. In Never Rest on Your Ores Keevil’s son Norman, also a geoscientist, recounts how over the next fifty years, a growing team of like-minded engineers and entrepreneurs built Canada’s largest diversified mining company. In candid detail he tells the story of a company and its makers, of the discovery and creation of mines, of the mechanics of industry financing, and of the role that mergers and acquisitions play in a volatile environment. Along the way he meets fascinating captains of industry and politicians not only in Canada, but in the United States and around the world. Finding an ore body – rock that holds valuable metals and minerals – and promoting its development in order to finance and create a mine, most often in hard-to-access wilderness, is complicated work, comparable to locating and extracting a needle in a very messy haystack. Underlying this history is a constant need to replenish the ore, and this need drives the people involved. Drawing new lessons from the turbulent period between 2005 and 2023, this new edition of Never Rest on Your Ores is both entertaining and instructive, a rare insider’s account of an industry that has been crucial to the building of this country.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.064 |
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".