Decomposition of stumps 10 years after partial and complete harvesting in a southern boreal forest in Finland
Bibliographic record
Abstract
We studied the decomposition of cut stumps of Norway spruce ( Picea abies (L.) Karst.), Scots pine ( Pinus sylvestris L.), and birches ( Betula pubescens Ehrh. and Betula pendula Roth.) 10 years after clear felling, low level retention felling, gap felling, and selection felling. Bulk density of wood, mass per surface area of bark, and mass of wood and bark for entire stumps were estimated. Using a single exponential model, annual decomposition rate constants (k) were calculated as 0.071, 0.052, and 0.041 ·year–1 for birch, spruce, and pine, respectively. The k values for wood decreased in the same order. For bark, the order was different: spruce bark decomposed slower than pine bark. Fragmentation accelerated mass loss. Pine and birch bark decomposed faster than pine and birch wood, whereas spruce showed the opposite tendency. The wood density and bark mass did not depend on retention levels. Diameter of stumps did not explain variation in decomposition either. The high importance of stumps for biodiversity, carbon, and nutrient cycling requires refinements to decomposition rate constants. Thus, further research based on new empirical data and meta-analysis of published data is needed to reveal factors influencing the decomposition process in situ.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".