Knowledge Exchange in the Canadian Wood Fibre Centre: National scope with regional delivery
Bibliographic record
Abstract
Since its inception in 2006, the Canadian Wood Fibre Centre (CWFC), a branch of Natural Resources Canada’s Canadian Forest Service, has placed an emphasis on Knowledge Exchange (KE). KE at the CWFC has followed a progression from raising awareness, to generating interest, through to providing support for those deciding to adopt an innovation. Designing a program of national scope with regional delivery has led the CWFC to partner with different regional delivery organizations. Across Canada, Light Detection and Ranging (LiDAR) based Enhanced Forest Inventory has become a very successful innovation spearheaded by the CWFC in partnership with academia, the forest industry, and a growing consulting sector. The KE program for LiDAR based Enhanced Forest Inventory is used as an example to illustrate the CWFC’s KE methods along with a description of regional delivery agencies and the success in getting this innovation adopted.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".