Bibliographic record
Abstract
The federal forest service, originating in 1899, began with Elihu Stewart as the chief inspector of timber and forestry and the title of Superintendent of Forestry; it grew to become the Dominion Forestry Branch. Until 1930 the main federal forest lands were in the western provinces where major agricultural settlement was taking place and the main priorities were fire protection and tree planting. In 1924, E.H. Finlayson a 1912 graduate in forestry became the first Dominion Forester; he was succeeded by D. Roy Cameron as the second Dominion Forester and when he left in 1947 Donald Angus Macdonald became the third and last Dominion Forester until his retirement in 1956. Macdonald’s career bridged the early period of the federal forestry branch’s activities of forest management and protection and into the post 1930s when the priorities were on research and the establishment of experimental forests. Following World War II Macdonald was instrumental in the crafting of the Canada Forestry Act of 1949, which led to the implementation of major provincial forestry activities such as forest inventories, regeneration and forest protection. It was followed by a succession of federal–provincial agreements, which have left an underpinning for forestry across the nation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".