Intensive management increases flexibility in managing wood supply
Bibliographic record
Abstract
We modelled the potential impact of intensification of plantation management and thinning, on timber supply of an ∼500 000 ha forest in Alberta, Canada over a 200-year planning horizon. Pre-commercial and commercial thinning were applied to a portion of the better sites, which allowed shortening of the time to merchantability and earlier harvest than unthinned stands; less than 25% of the forest was thinned over the course of the plan. The sustainable harvest rate, represented here as the annual allowable cut (AAC), increased by ∼14% above baseline, when thinning was applied. Similarly, there was a 20.7% increase in AAC projected with the relaxation of even flow rules if thinning was also applied. Finally, thinning offset the expected decline in AAC after a 20-year surge in cutting of mature pine, designed to slow an epidemic of mountain pine beetle. In terms of wood supply, the volume extracted at thinning was <2% of annual supply but volume from final harvest from thinned stands, at times, surpassed that of unthinned stands of the forest. Individual tree size from thinned stands was ∼twice that from unthinned areas in the second part of the planning period, offsetting the expected decline in piece size after the era of harvest of natural forests.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".