Coarse woody debris in forest regions of Russia
Bibliographic record
Abstract
To assess regional stores of coarse woody debris (CWD) in seven major forest regions of Russia, we combined data collected as part of the routine forest inventory with measurements in 1044 sample plots and the results of density sampling of 922 dead trees. The stores of CWD in the western part of Russia (St. Petersburg, Central, Khanty-Mansi, and Novosibirsk regions) were on average lower (1420 m3/ha or 4.05.8 Mg/ha) than in the East Siberian and Far Eastern regions (4051 m3/ha or 11.014.4 Mg/ha). The difference in CWD stores was particularly large between young forests in two western regions (2.4 Mg/ha in St. Petersburg and 3.4 Mg/ha in the Central region) and in the east (20.424.4 Mg/ha). This difference is associated with the prevailing disturbance type: clear-cut harvest in western Russia and natural disturbances in the east. Analysis of variance in CWD stores indicates that region, dominant species, forest age group, productivity class, and interactions of these factors explain 8788% of the total variance and the strongest effects are for age group and region. Lower stores of CWD within the intensively managed forest regions suggest that further expansion of forest use in many regions of Russia may reduce regional stores of CWD and carbon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".