Modeling dead wood in Fennoscandian old-growth forests dominated by Norway spruce
Bibliographic record
Abstract
If equilibrium is assumed in unmanaged forests, the volume of coarse woody debris (CWD), VCWD, may be calculated from (i) the volume of living trees, Vliving, (ii) average volume of a dead stem in relation to when it was alive, k, (iii) tree mortality rate, m, and (iv) residence time of CWD, t, by the equation VCWD = Vlivingkmt. We parameterized this equation with data from Norway spruce (Picea abies (L.) Karst.) dominated forests in Fennoscandia. The Vliving was assumed to be directly proportional to forest productivity. Tree mortality data were from the National Forest Inventory, while it was difficult to find quantitative data on k and t. The predicted amounts (74138 m3/ha, with larger amounts in the south) and size distribution (a negative exponential distribution of the number of stems) of CWD corresponded fairly well to averages from field inventories. By using a computer simulation program, the variability in tree mortality, density of living trees, and residence time of CWD were considered. In the simulations, the amount of CWD varied widely between 1-ha plots, especially for individual decay classes. Therefore, this model could be used to predict averages from larger landscapes unaffected by large disturbances, while no model can predict the amount of CWD at individual plots.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".