Understory plant diversity and composition across a postfire tree density gradient in a Siberian Arctic boreal forest
Bibliographic record
Abstract
Cajander larch (Larix cajanderi Mayr.) forests of the Siberian Arctic are experiencing increased wildfire activity in conjunction with climate warming. These shifts could affect postfire variation in the density and arrangement of trees and understory plant communities. To better understand how understory plant composition, abundance, and diversity vary with tree density, we surveyed understory plant communities and stand characteristics (e.g., canopy cover, active layer depth, and soil organic layer depth) within 25 stands representing a density gradient of similarly-aged larch trees that established following a 1940 fire near Cherskiy, Russia. Understory plant diversity and mean total plant abundance decreased with increased canopy cover. Canopy cover was also the most important variable affecting individual species’ abundances. In general, tall shrubs (e.g., Betula nana subsp. exilis) were more abundant in low-density stands with high light availability, and mosses (e.g., Sanionia spp.) were more abundant in high-density stands with low light availability. These results provide evidence that postfire variation in tree recruitment affects understory plant community composition and diversity as stands mature. Therefore, projected increases in wildfire activity in the Siberian Arctic could have cascading impacts on forest structure and composition in both overstory and understory plant communities.
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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".