A comprehensive framework to evaluate the financial impacts of genetic improvement on wood products from planted forests
Bibliographic record
Abstract
Increasing the productivity of planted forests may efficiently provide an important part of the world’s growing demand for wood while protecting natural forests. In this study, we developed an integrated modelling framework to evaluate the financial impacts of improving productivity of planted forests by tree breeding. Using this framework, we compared three genetic improvement scenarios of white spruce plantations, a key reforestation species in North America, and evaluated the differences in the derived wood product assortments in terms of quantity, quality, and revenues. Favouring the production of wood volume appears as the best way to enhance financial gains from white spruce plantations in the current market. The scenario that focused on increasing tree height produced a greater volume of wood products and larger lumber pieces, which resulted in the greatest revenues. In comparison, favouring wood stiffness over volume led to poorer results, as the increased product quality was not sufficient to surpass the financial gain associated with greater wood volumes. While we successfully provided an evaluation of the product assortments derived from genetically improved plantations, the proposed framework would benefit from more data input to help maximize financial gains from a range of tree breeding strategies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".