ANTHROPOGENICALLY TRANSFORMED GEOSYSTEMS OF THE SOUTHERN PART OF THE LENA-ANGARA PLATEAU
Bibliographic record
Abstract
The history of economic reclaiming taiga geosystems of the southern part of the Lena-Angara Plateau is examined with a research objective of their anthropogenic transformation. Fluctuating changes of the intensity of the agricultural and forestry activity of local population are pointed out. Agricultural area enlarged and the industrial development of forest resources increased for the time interval since the late 19th century till 1980s. Since the end of the 20th century the economic activity decreases that is associated with the state reorganization of national economy and with the creation within the studied site of the nature reserve. The map of the modern landscape structure has been compiled. Its analysis showed that despite the lack of permanent settlements and low activity of production facilities, about half of the study area is occupied by the transformed complexes. Forest fires became the reason of this process. Their appearance on the territory of the Lena-Angara Plateau is associated with human activity. Based on historical and geographical materials the evaluation of the recovery dynamics of the transformed geosystems is carried out. In lack of an anthropogenous factor on restoration natural mountain taiga larch forests will be required about 70 years; for emergence of the cedar forests which are absent now, perhaps, centuries are necessary.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".