Bibliographic record
Abstract
The mixed forest of the St. Lawrence valley, which presents jour successive landscapes during its seasonal rhythm, bas been the dorsal spine of Eastern Canada s economy since the establishment of the French colony. The various people who have successively inhabited this forest have either used it as members of the bio-sociological unit or tried to modify its ecology, depending on their traditional culture. It was occupied soon after the glacier recession by the Red-ochre Man, who was followed by the Algonkian forest hunters. Later, the same territory was inhabited by Iroquoian tribes, who brought with them their agriculture which had evolved in the South, but was reoccupied by the Algonkian tribes just before the foundation of Québec. At this time it became a country of European settlers, who carried with them their Old World agriculture and tried to reconstruct in a new continent their Normandie or Poitou landscape. For a newly established agriculturist, the land hardly produced enough for a living. The exploitation of Canadian forests was unpopular amongst the LaRochelle merchants who preferred to trade in the Baltic regions. The first important economic resource was the fur trade. Later, when Napoleon Bonaparte set up a blockade in the Baltic sea, England had to look elsewhere to save and develop her navy and found in the forests of Eastern Canada the pine-trees she needed. Finally, the increase in the number of news-papers, which was largely a consequence of the French revolution, developed another type of forest industry, the production of spruce pulp.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".