Comparative Analysis of Boreal Forest Landscape Processes Using SELES: Russian Versus Finnish Karelia
Bibliographic record
Abstract
The border between Finland and Russia is characterized by a sharp and distinctive change in the structure and age composition of the resident boreal forest. The abundance of large areas of relatively intact old-growth forests on the Russian side adjacent to an area of intense management on the Finnish side provides a unique reference area for comparative forest ecological investigations. In this paper, an analysis of the landscape processes active in these two management areas is initiated through the prototyping and refinement of a cell-based dynamic landscape model. Through development of this model, we seek to explore the effects of various management plans on the biodiversity of the region. Inputs to this landscape ecological planning model are 1) raster GIS layers and 2) process models. The GIS layers are derived from classified satellite (Landsat TM and Spot) imagery, digital elevation models, and vegetation and logging prescription maps. Process models (e.g. logging, succession) are taken from the literature. The landscape modeling tool used in the research, called SELES, was developed at Simon Fraser University, Canada. 1. Background Recently, the ecological consequences of intensive forest use for fibre extraction have been strongly
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 | 0.001 |
| 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".