Beyond the edge: Environmental characteristics of northwestern Eurasian primary forests contrast with surrounding areas
Bibliographic record
Abstract
Abstract The area of primary boreal forest continues to decline due to anthropogenic disturbance, often targeting forests that provide the highest economic returns. This selective use of forests raises the question of whether the remaining primary forests occur within a subset of the environmental conditions present in their region of occurrence. We investigated whether and how the environmental conditions of primary boreal forests in Finland and northwestern Russia (Arkhangelsk, Karelia, Komi, and Murmansk) differ from those of their surrounding forests. To do this, we randomly selected 50 primary forests from each region and used openly available spatial data to quantify a set of variables describing topography, land cover, and accessibility for these primary forests and their surrounding forests. The remnant primary forests had different environmental characteristics compared with the surrounding forests in each study region. In terms of topography, the primary forests had either a higher absolute elevation or a higher topographic position than the surrounding forests. In Finland, the distance to rivers was also significantly higher in primary forests than in surrounding forests. The proportion of wetlands was high in the primary forests of Finland and Karelia, suggesting a high proportion of primary forests on organic soils. For all variables, the magnitude and occasionally even the direction of the difference between primary and surrounding forests varied between regions. In Finland and European Russia, the distribution of the remnant primary forests does not represent the full environmental variability present in their region of occurrence. This suggests that these forests do not only occur “high and far,” but within a subset of the environmental conditions present in these high and far regions. From a conservation perspective, primary forest attributes should be restored regionally, taking into account the diversity of environmental conditions that exist within the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".