Effects of climate, disturbance, and species on forest biomass across Russia
Bibliographic record
Abstract
We used detailed forest inventory data from 43 forests (3.5 × 103 115.2 × 103 stands each) and meteorological data from 30 weather stations located in proximity to these forests to assess the effects of disturbance and climate on biomass accumulation patterns across the forest zone of Russia. Chronosequences of biomass accumulation following disturbance were developed for each of the two to five dominant tree species in each forest using stand survey data collected by forest inventories in different regions of Russia between 1986 and 2003. These chronosequences represent changes in average live biomass of forest stands between age 10 and 210 years at 10-year intervals. The correlation of attributes of biomass accumulation (i.e., maximum biomass, biomass at age 40, and maximum biomass increment) with climatic and disturbance attributes was significant but weak (adjusted R2 = 0.200.37). The effect of the most influential disturbance attributes (percent clear-cut and percent old forest) was as strong or stronger than the effect of climatic attributes (30-year averages of the sum of positive daily temperatures and climate moisture index). The effect of tree species was significant, but weaker than the effects of climate or disturbance. Combining climate, disturbance, and species attributes generally improved the models (adjusted R2 = 0.370.53). The patterns of biomass change observed in chronosequences are influenced by the tendency of harvesting to target more productive forest stands of commercially valuable species, creating a disparity in productivity among the age cohorts. The apparent link between disturbance attributes of forests and biomass accumulation patterms in forest stands may be used to improve broad-scale modeling of changes in forest biomass with remotely sensed data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".