Biomass and dynamics of attached dead branches in the canopy of 450-year-old Douglas-fir trees
Bibliographic record
Abstract
The amount, physical characteristics, and spatial distribution of attached dead branches in the canopy of 450-year-old Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesii) trees were studied over a 5-year period (19982003) to quantify their contribution to the canopy woody detritus pool of an old-growth Douglas-fir western hemlock (Tsuga heterophylla (Raf.) Sarg.) forest in the western Washington Cascades. We developed a five-class decay rating for attached dead branches. From the size distribution and relative amounts and vertical distribution of attached dead branches in the five decay classes, we inferred that death of large original branches had occurred in the recent past, followed by the production and death of epicormic branches. Tree height was an important variable for predicting branchwood dry mass per tree. We estimated that attached dead branches of Douglas-fir contributed 63.8% of the total canopy woody detritus pool of the stand. During the study period, 0.24%·year 1 of live branchwood mass died attached, and 0.37%·year 1 and 4.34%·year 1 , respectively, of live- and dead-branch mass were lost due to branchfall and fragmentation. Because branch death and branchfall are stochastic processes, long-term monitoring in the canopy and on the ground is needed to understand the dynamics of canopy woody detritus.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".