The dynamics of coarse woody debris in boreal Swedish forests are similar between stream channels and adjacent riparian forests
Bibliographic record
Abstract
Although numerous studies have focused on the dynamics of coarse woody debris (CWD) in boreal Fennoscandian forests, information on CWD in streams remains limited. To achieve a better understanding of CWD dynamics in streams we compared amounts and characteristics of CWD between streams and adjacent riparian forests in old-growth and managed forest sites, respectively. We also identified distances to the sources of CWD and evaluated these in relation to the lateral zonation of riparian trees. CWD volumes found in the stream channels were related to, but exceeded, the volumes found in the adjacent forest. In-channel volumes separated by species were better correlated with terrestrial volumes of CWD than with volumes of living trees. Tree species appeared to be zoned across the riparian zone, with slightly higher abundances of deciduous trees and lower abundances of Scots pine trees close to the stream. Similar to upland forests, riparian forests were dominated by coniferous tree species, mainly Norway spruce (Picea abies (L.) Karst.). These findings suggest large similarities in CWD input between streams and riparian forests and substantially slower decomposition rates in stream channels compared with those in riparian forest. The results provide an improved basis for creating reliable models of CWD supply and maintenance in streams based on knowledge of forest development and CWD dynamics in the terrestrial environment. Site productivity could potentially be used to predict CWD volumes in stream channels under pristine conditions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".