Changes in litter and dead wood loads following tree death beneath subalpine conifer species in northern Colorado
Bibliographic record
Abstract
Litter and dead wood affect important processes in forest ecosystems such as nutrient and carbon cycling and are key influences on biodiversity and fire behavior. Increased tree mortality rates in western North America associated with climate trends and increased bark beetle activity highlight the need to better understand the dynamics of litter and dead wood following tree death. For eight old-growth stands in a subalpine forest landscape in northern Colorado (USA), we compared litter and dead wood loads beneath more than 200 dead and live Engelmann spruce (Picea engelmannii Parry ex Engelm.), subalpine fir (Abies lasiocarpa (Hook.) Nutt.), and lodgepole pine (Pinus contorta Douglas ex Loudon). The dynamics of litter and dead wood were analyzed using chronosequences of tree death dates over >100 years that we determined from tree rings. Immediately following tree death, high loads of litter accumulated, particularly for the biggest spruces, which accumulated 10 times more litter than live spruces (five times more for fir, two times more for pine). We estimated a higher decay rate of litter for spruce (half-life of four years) than for pine (15 years) and fir (19 years). The accumulation rates for dead wood following tree death were highly variable among trees, but maximum accumulation was attained during the first 50–60 years.
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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 | 0.000 | 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".