Watershed-scale forest biomass distribution in a perhumid temperate rainforest as driven by topographic, soil, and disturbance variables
Bibliographic record
Abstract
Temperate rainforests are the most carbon dense forest ecosystem on the planet, with C stocks several times higher than most other forested biomes. While climatic and disturbance drivers of these C stocks are relatively well explored, the spatial distribution of those stocks at the scale of entire watersheds is less well known, particularly in perhumid rainforests where research has been minimal. This study explored biomass distributions across an entire watershed simultaneously, from ocean to glacial icefields, in Southeast Alaska. Utilizing LiDAR and ground surveys, biomass was modelled throughout the landscape and distributions are described statistically. The dominant driver of biomass distributions at this scale (controlling for elevation) was the flow of water through the landscape: areas of higher water accumulation typically had low biomass (often <10 Mg·ha–1), whereas well-drained areas supported biomass approaching 950 Mg·ha–1. This relationship was strong at all elevations; only riparian locations (typically well-drained soils) maintained high biomass at low slopes. Exposure to stand-replacing disturbances, often a dominant driver, was only a minor factor. This work emphasizes the importance of water in temperate rainforests and the potentially significant impacts of changes to biomass given changes in precipitation (both increasing and decreasing) due to global climate change.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".