Bibliographic record
Abstract
Like many of my generation, I continue to be confounded by North America's split personality when it comes to everyday measurement of temperature, distance, volume, and weight.And this split does not split right at the Canada-U.S. border.As a pilot in Canada I still spend my working days conversing and calculating in miles and feet and pounds, for these are the worldwide industry standards.Meanwhile, as a Canadian citizen I am encouraged by Ottawa to think strictly in kilometres, litres, and grams.Alas, as an Illinois boy born in 1957, my mind will forever run in miles and inches and pounds.In this book I have given temperatures and distances and other measurements in the way they now come naturally to me, and to many millions of others like me: Temperatures in degrees Celsius (for after twenty-seven years in Canada, I have at least mastered that changeover), distances in statute miles, smaller measurements in inches and feet, and weights in pounds.This is the way I think and the way everyone I work with, right across western Canada, still talks.Maybe in another generation or two we will all be converted and toeing the metric line.As for the terms and slang of dog mushing and winter bush travel, a glossary is provided at the back of the book, along with several appendices, which might help the reader to understand some of the details of my travel and camping methods, food supplies, dog care, and navigation.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.393 | 0.287 |
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".