Advancing the application of remote sensing for forest information needs in Canada: Lessons learned from a national collaboration of university, industrial and government stakeholders
Bibliographic record
Abstract
The Canadian forest sector requires detailed information regarding the amount and characteristics of the forest resource. To address these needs, inventory systems that spatially quantify timber and other forest related ecosystem services are required, that are accurate, comprehensive and timely. The Assessment of Wood properties using Remote Sensing (AWARE) was a five-year project involving collaboration between seven Canadian universities, and seven forest companies with support provided by provincial and federal forestry agencies and other non-for-profit forestry focused organisations. AWARE provided methods and tools to enhance the characterization of forests at national, landscape and individual tree scales. The project supported 24 post-doctoral fellows, PhD and MSc students that examined the roles that advanced three-dimensional remote sensing technologies can play in the development of accurate forest inventory systems across Canada. In this review we examine the AWARE research project, review research highlights, key outcomes, future research needs, and provide an assessment of successes and challenges the project faced over its five-year lifetime.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".