Amount, position, and age of coarse wood influence litter decomposition in postfire<i>Pinus contorta</i>stands
Bibliographic record
Abstract
Spatial variation in vegetation and coarse wood is a major source of forest heterogeneity, yet little is known about how this affects ecosystem processes. In 15-year-old postfire lodgepole pine (Pinus contorta var. latifolia Englem.) stands in Yellowstone National Park, Wyoming, we investigated how the decomposition rate varies with the position of coarse wood and other dominant structures within and among stands. Tongue depressors (TD) (made of birch (Betula sp.)) and litterbags containing herbaceous litter (HL) and needle litter (NL) were deployed for 2 years within 3 burned stands and among 17 burned stands (each 0.25 ha). Within stands, the decomposition rate varied among six microsite treatments (above and below legacy wood, below logs on the ground and elevated logs, below saplings, and on open soil). Two-year mean mass loss from all litter types was least under elevated logs (HL 34.0%, NL 8.6%, TD 3.5%) and greatest under legacy wood (HL 55%, NL 33%, TD 12%). The moisture level was consistently lowest under elevated logs and highest beneath logs on the ground. Among forest stands, 2-year mass losses from HL and TD were negatively related to the amount of elevated wood. The influence of coarse wood on litter decomposition at two spatial scales suggests that coarse-wood accumulation creates long-term spatial heterogeneity in carbon and nutrient cycles.
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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) | 0.001 | 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".