Satisfying rival forestry objectives in the Komi Republic: effects of Russian zoning policy change on wood production and riparian forest conservation
Bibliographic record
Abstract
Spatial segregation of different forest landscape functions can accommodate rival forestry objectives more comprehensively than integrated approaches. Russia has a unique history of forest zoning separating production and environmental functions. However, the Russian Forest Code of 2006 increased the focus on wood production. We reviewed the history of zoning policy in Russia and assessed if the recent policy change affected logging rates and conservation of riparian forests. Using Russia’s Komi Republic as a case study, we specifically assessed (i) if policy change led to increased logging near streams, (ii) if logging rates were different in headwaters vs. main rivers, and (iii) how logging changed among catchments with different accessibility to logging. Using a global open-access remote sensing dataset, we compared mean annual forest loss as a proxy of logging rates in 10 large forested catchments in the Komi Republic in one period with strict zoning policy (2000–2006) and one with moderate zoning policy (2007–2014). Harvesting rate was positively related to the distance from streams. On the other hand, it increased after the policy change in the buffer zone but decreased outside it. Forests were harvested more in headwater buffers than along larger rivers, and harvest in the catchments near industries was higher and increasing; remote catchments had low forest loss. We discuss the opportunity for adopting forest zoning policy in different governance contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".