Map of Forest Management in Canada, 2017 version
Bibliographic record
Abstract
Forests cover large areas of Canada but only some of these forests are actively managed. The Map of Forest Management in Canada, 2017 version, provides a generalized classification of forest management in Canada, including: protected areas, Treaty/Settlement Lands (including Treaty Lands identified in Final Agreements, Land Claim Agreements and Settlements), Indian Reserves, other federal reserves (including military training areas), provincial and territorial reserves and restricted use areas, private lands, short- and long-term Crown forest tenure areas and areas with no current Crown timber dispositions. The Managed Forest Map of Canada dataset provides a wall-to-wall classification of lands in Canada. It does not differentiate areas of forest from non-forest. The Managed Forest Map of Canada differs from maps defining the area designated as “managed forest” for greenhouse gas inventory reporting purposes and does not replace those maps. Instead, the Managed Forest Map of Canada shows areas that are currently managed, as of June 2017, and provides generalized management type classification for those areas. Collaborating agencies plan to update the dataset periodically as needed, and remain open to receiving advice from experts concerning refinement priorities for future versions.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.026 |
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".