Simulated conversion of unmanaged interior spruce-subalpine fir stands to a regulated uneven-aged structure
Bibliographic record
Abstract
A localized version of the Forest Vegetation Simulator (FVS) individual tree growth model was developed to simulate stand level impacts under a variety of uneven-aged management regimes in old-growth interior spruce-subalpine fir forests near Prince George British Columbia. Options for using uneven-aged management to satisfy a range of management objectives were simulated using different sets of BDq regulation parameters, and by varying species composition and rules for reserve trees. The greatest timber yields were attained by promoting the highest possible spruce component, using high q ratios, low to moderate maximum diameters and 20 to 25 year cutting cycles, and allowing no reserves. Using strategies to promote stand structures maintaining some old-growth attributes resulted in much lower timber yields. Key words: white spruce, Engelmann spruce, subalpine fir, uneven-aged management, BDq regulation, simulated yield
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".