Monitoring Canada's forests: The National Forest Inventory
Bibliographic record
Abstract
A new national forest inventory is being installed in Canada. For the last 20 years, Canada's forest inventory has been a compilation of inventory data from across the country. Although this method has a number of advantages, it lacks information about the nature and rate of changes to the resource, and does not permit projections or forecasts. To address these limitations a new National Forest Inventory (NFI) was developed to monitor Canada's progress in meeting a commitment towards sustainable forest management, and to satisfy requirements for national and international reporting. The purpose of the new inventory is to "assess and monitor the extent, state and sustainable development of Canada's forests in a timely and accurate manner." The NFI consists of a plot-based system of permanent observational units located on a national grid. A combination of ground plot, photo plot and remote sensing data are used to capture a set of basic attributes that are used to derive indicators of sustainability. To meet the monitoring needs a re-measurement strategy and framework to guide the development of change estimation procedures has been worked out. A plan for implementation has been drafted. The proposed plan is presented and discussed in this paper. Key words: Canada, forest cover, inventory, monitoring, National Forest Inventory, re-measurement, panel
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".