A trait-based approach to both forestry and timber building can synchronize forest harvest and resilience
Bibliographic record
Abstract
Along with forest managers, builders are key change agents of forest ecosystems' structure and composition through the specification and use of wood products. New forest management approaches are being advocated to increase the resilience and adaptability of forests to climate change and other natural disturbances. Such approaches call for a diversification of our forests based on species' functional traits that will dramatically change the harvested species composition, volume, and output of our forested landscapes. This calls for the wood-building industry to adapt its ways of operating. Accordingly, we expand the evaluation of the ecological resilience of forest ecosystems based on functional diversification to include a trait-based approach to building with wood. This trait-based plant-building framework can illustrate how forecasted forest changes in the coming decades may impact and guide decisions about wood-building practices, policies, and specifications. We apply this approach using a fragmented rural landscape in temperate southeastern Canada. We link seven functional groups based on the ecological traits of tree species in the region to a similar functional grouping of building traits to characterize the push and pull of managing forests and wood buildings together. We relied on a process-based forest landscape model to simulate long-term forest dynamics and timber harvesting to evaluate how various novel management approaches will interact with the changing global environment to affect the forest-building relationships. Our results suggest that adopting a whole system, plant-building approach to forests and wood buildings, is key to enhancing forest ecological and timber construction industry resilience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".