Characterizing the wood attributes of Canadian tree species: A thirty-year chronicle
Bibliographic record
Abstract
In 2007 Forintek Canada Corp merged with the other forest research institutes—Paprican, FERIC and the newly formed Canadian Wood Fibre Centre (CWFC)—to become FPInnovations. This merger offers opportunities for synergies across a range of research activities from the forest to final product markets, and the first step to achieving these synergies is to provide a better understanding of past and current research roles. This paper chronicles delivered results from the Resource Program in response to Forintek member priorities. Of necessity due to limited resources, the Resource Assessment Program at Forintek was built on both internal and external collaboration. It was also built on a legacy of wood quality research inherited from the Eastern and Western Wood Product Laboratories of the Canadian Forest Service from which it was formed through privatization in 1979. FPInnovations now has custody of this legacy. This paper was prepared as a contribution to a workshop organized by the CWFC to promote better understanding of research capabilities residing within FPInnovations. It is aimed at identifying opportunities for future collaboration by describing Forintek's resource characterization program and our members' priorities for future wood quality research in Canada. Key words: resource characterization, wood quality, stand management, future forests, present forests, CT imaging, product Recovery
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".