Forest inventory research at the Canadian Wood Fibre Centre: Notes from a research coordination workshop, June 3–4, 2009, Pointe Claire, QC
Bibliographic record
Abstract
As part of its mandate to improve forest sector competitiveness, the Canadian Wood Fibre Centre (CWFC) has directed its research program towards the enhancement of forest inventory tools and systems to enable the spatial identification and forecasting of forest value. A June 2009 workshop attended by inventory researchers and provincial specialists from across the country reviewed current research needs and made recommendations for improvement of the CWFC-funded research program over the next 2- to 5-year period. Current efforts appear well positioned to improve stand-level inventory detail through cost-effective, semi-automated interpretation and quantification of tree species, size, and distribution. Means of incorporating specific attributes to better convey value, such as branch size and wood density, are being actively explored. More effort and resources are required to meet needs for better predictive models, sampling systems that incorporate data from a wide array of available sources, and proactive technology transfer and uptake of research results. Key words: forest inventory, value chain optimization, fibre quality, LiDAR, multispectral digital imagery
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".