Biophysical controls on surface fuel litterfall and decomposition in the northern Rocky Mountains, USA
Bibliographic record
Abstract
Litterfall and decomposition rates of the organic matter that comprise forest fuels are important to fire management, because they define fuel treatment longevity and provide parameters to design, test, and validate ecosystem models. This study explores the environmental factors that control litterfall and decomposition in the context of fuel management for several major forest types in the northern Rocky Mountains (Idaho and Montana), USA. Litterfall was measured for more than 10 years using semiannual collections of six fine fuel components (fallen foliage, twigs, branches, large branches, logs, and all other canopy material) collected from a network of 1 m2 litterfall traps installed at 28 plots across seven sites. Decomposition of foliage, twigs, branches, and large branches were measured using litter bags installed on five of the seven sites. Measured litterfall and decomposition rates were correlated with major environmental and vegetation variables using regression analysis. Annual foliage litterfall rates ranged from 0.057 kg·m–2·year–1 for dry Pinus ponderosa Dougl. ex Laws. stands to 0.144 kg·m–2·year–1 on mesic Thuja plicata Donn ex D. Don stands and were correlated with the vegetation characteristics of leaf area index, basal area, and tree height (r > 0.5), whereas decomposition rates were correlated with the environmental gradients of temperature and relative humidity (r > 0.4).
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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 | 0.000 |
| 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".