CANADA'S FOREST COVER INDICATOR: DEFINITION, METHODOLOGY AND RESULTS
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT. The Environment and Sustainable Development Indicators (ESDI) Initiative was introduced to track Canada's overall wealth in the form of natural and human capital, in additionto familiar economic data such as the gross domestic product (GDP). One of the six ESDIs is the Forest Cover Indicator (FCI). In this paper we define FCI, outline the overall method for deriving FCI, and report results for addressing four key technical issues in carrying out this overall method. The FCI is defined as interannual variations of Canada's forest area with the middle-summer crown closure (CC) ? 10%. Crown closure is the percentage of the ground surface covered by a downward vertical projection of the tree crowns. Theoverall monitoring method is mainly based on coarse resolution remote sensing data because of the need to cover Canada's extensive landmass during the middle-summer months and toupdate the results annually. Medium resolution satellite data, field measurements, and modeling approaches were used for calibration, correction, validation, and down-scaling, with a focus on the following 4 key technical issues: (1) correcting understory non-tree vegetation effect on CC, (2) downscaling forest cover area from 1-km to 100-m spatial resolution as required by the FCI definition, (3) detecting the changes of CC caused by disturbances, and (4) detecting changes in CC caused by forest regrowth. Methods and results for addressing these technical issues are described in the paper. While these results indicate that the key technical issues can be solved by integrating satellite remote sensing data/products and other data, there are clear needs for further development, especially testing against field measurements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it