Bibliographic record
Abstract
The first edition of this book, written in 2015 and published in 2017, told a collection of stories over almost 100 years from the start of Teck-Hughes Gold Mines Limited on a gold discovery at Kirkland Lake, Ontario; the contemporaneous birth of my father, also Norman B. Keevil, in a hamlet in Saskatchewan; and how the two threads came together some 50 years later to create Teck Corporation, an adventurous producer of copper, gold, silver, and oil with aspirations to become a major force in Canadian mining.It told how Teck grew from modest beginnings by finding and developing a succession of new mines, adding niobium, zinc, and metallurgical coal to the portfolio, managed by a C-suite that included the two Keevils, father and son, mining icon Robert Hallbauer, and financial guru David Thompson, with the support of countless colleagues.Between 1975 and 2005 alone Teck developed or acquired 17 new mines, a growth period that culminated in Teck's participation in a major copper and zinc mine in Peru and the consolidation of its Elkview mine with five additional independent Canadian coal mines into the Elk Valley coal partnership, managed and effectively controlled by Teck.In the process, Teck grew from a $25 million company in 1975 into a $12.6 billion company in 2005.The stories of the first edition essentially ended there, as the author and Thompson retired from their successive ceo roles and Don Lindsay became ceo.Chapter 42, "The Last Decade," touched on some events in the ensuing years that began with the new "China super-cycle," an almost unprecedented commodities boom that began to be noticed around 2005.But, as we observed in that edition, "the tales of the last 10 years have, for the most part, yet to mature, let alone be finished.So I will just note briefly some of the main events and works in progress.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.318 | 0.230 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".