Effectiveness of forest management strategies to mitigate effects of global change in south-central Siberia
Bibliographic record
Abstract
We investigated questions about the ability of broad silvicultural strategies to achieve multiple objectives (reduce disturbance losses, maintain the abundance of preferred species, mitigate fragmentation and loss of age-class diversity, and sequester aboveground carbon) under future climate conditions in Siberia. We conducted a factorial experiment using the LANDIS-II landscape disturbance and succession model. Treatments included varying the size and amount of areas cut and the cutting method (selective or clearcut). Simultaneously, the model simulated natural disturbances (fire, wind, insect outbreaks) and forest succession under projected future climate conditions as predicted by an ensemble of global circulation models. The cutting method and cutting rate treatments generally had a large effect on species and age-class composition, residual living biomass, and susceptibility to disturbance, whereas cutblock size had no effect. Cutblock size affected only measures of fragmentation, but cutting method and cutting rate often had an even greater effect. Based on the results, we simulated a “recommended” strategy and compared it with the current forest management practice. The recommended strategy resulted in greater forest biomass, increased abundance of favored species, and reduced fragmentation, but it did not significantly reduce losses by disturbance. No single strategy appears able to achieve all possible forest management objectives.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".