Recruitment of wood to streams in old-growth and second-growth redwood forests, northern California, U.S.A.
Bibliographic record
Abstract
From an ecological perspective, one aim of forest management is to supply wood to streams to protect and enhance aquatic habitats. An analysis was made of the mass balance of in-stream wood along 9 km of channels in old-growth and 50-year-old second-growth redwood (Sequoia sempervirens (D. Don) Endl.) forests in northern California, U.S.A. High volumes of wood storage in streams in old-growth forests were due primarily to streamside landsliding and bank erosion. Logging-related debris and high forest mortality rates in conifer and deciduous forests contributed to high wood storage in second-growth forests. Volumes of in-stream wood in second-growth forests were similar to volumes in one old-growth system and less than another. Diameters of wood were significantly greater in older forests. Wood recruitment from forest mortality in old-growth forests was low compared with second-growth sites, driven by differences in conifer mortality rates of approximately 0.04 and 0.9%·year1, respectively. Contrasting old-growth redwood mortality with values reported for unmanaged Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests in Washington State (0.5%·year1) and unmanaged Sitka spruce (Picea stichensis (Bong.) Carrière) forests in southeastern Alaska (1.2%·year1) point to a strong latitudinal gradient of forest mortality reflected in tree size. The mass balance analysis of in-stream wood also allowed an estimation of bank erosion along large channels and soil creep along small, steep streams.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".