Bibliographic record
Abstract
Foreword: Mon pays c'est le feu / Graeme Wynn Author's Note: A Boreal Bush Prologue: White Canada Book 1: Torch Kindling Fire Rings of Indigenous Canada Fire and Frost: Tundra Fire and Water: Boreal Forest Fire and Grass, Fire and Leaf: Great Plains Prairies and Great Lakes Forests Fire on the Hills, Fire on the Mountains: Acadian Woods and Cordilleran Forests Fire and Fog: The Incombustible Fringe Tongues of Fire: Black Spruce and High Plains Conflagration and Complex Book 2: Axe Creating Fuel Fire Frontiers of Imperial Canada New Found Land Acadia The Canadas Far Countries With Fire in Their Eyes: Gabriel Sagard and Henry Hind Burning Most Furiously Book 3: Engine Containing Combustion Reconnaissance by Fire: Robert Bell and Bernhard Fernow/ xxx Fire Provinces of Industrial Canada Dominion of Fire: Canada's Quest for Fire Conservancy Sea and Shield: Fire Provinces of Eastern Canada Fire's Lesser Dominion Tracer Index: James G. Wright and Herbert B. Beall Plain and Mountain: Fire Provinces of Western Canada Prosperity and Peril Two Solitudes: C.E. Van Wagner and Donald Stedman Revanchism and Federalism Fire's Outer Limits: Fire Provinces on the Fringe Internal Combustions Epilogue: Green Canada Continental Drift and Global Warming Fire Geography of Green Canada Fire's Reconfederation Settlement Symmetries, Then and Now Counting Carbon Virtual Fire Slow Burns, Fast Flames Fire and Ice Notes Bibliographic Essay Index
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".