Crown-fibre attribute relationships for enhanced forest inventory: Progress and prospects
Bibliographic record
Abstract
A five-year project, Crown-Fibre Attribute Relationships (CFAR), was completed by the Canadian Wood Fibre Centre (Natural Resources Canada) to explore the relationships between tree crown characteristics and wood fibre attributes. The CFAR project used a number of data sets distributed across Canada (e.g., silvicultural experiments), providing a range of crown conditions following spacing and thinning treatments. The approaches developed under the CFAR project, along with other relevant research, indicate that is possible to enhance current forest inventories by providing estimates of fibre attributes from crown characteristics of individual trees that can, or are poised to be, captured using remotely sensed data. Predictability was related to the dimensionality of the fibre attributes with models for zero-dimensionality fibre attributes (e.g., DBH) showing RMSE of 10% to 15% of mean values. Also, one-dimensional (e.g., sapwood area) and two-dimensional (i.e., ring area) models quantified longitudinal patterns with low bias. Current values of wood density were not related to crown characteristics; instead, recent research suggests that wood density is regulated by hydraulic and biomechanical constraints. Further evolution of remote sensing technology and related research will help to address the temporal problem posed by two- and three-dimensional fibre attributes.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".