DYNAMICS OF CHANGES IN THE FOREST FUND OF NATURAL RESERVE «DREVLYANSKY»
Bibliographic record
Abstract
The analysis of the dynamics of changes in the areas of land categories and the average tax indicatorsof the Drevlyansky Nature Reserve is carried out. It is established that the area of the forest fund of theReserve has not changed. There was an increase in the area of forest land covered by 106.9 hectares andin 2018 is 15021.1 hectares. The area of lands not covered with forest vegetation decreased by 107.5 ha, ofwhich the area of non-closed forest crops decreased by 104.5 ha. With a decrease in the area of forest landsby 0.6 ha, the area of non-forest lands (swamps) increased accordingly. There were also minor changesamong the taxonomic indicators of the stand. The average age of the stand increased by 5.5 years (from1.5 years the age of hanging birch increased to 6.5 years of aspen). The average credit rating decreased by0.16 (from 2.45 to 2.61). The largest decrease occurred by 0.8 in pine banks (from 1.8 to 2.6). The highestquality in Canadian poplar. There was also an increase in average fullness: from 0.78 in 2008 (mediumstand) to 0.81 in 2018 (high stand) the largest increase in fullness occurred in hanging birch — by 0.05(from 0.73 to 0.78 ). There are also stands with a density of 1.0, the area of which decreased in 2018 compared to 2008 by 107 hectares (from 356.8 hectares to 346.1 hectares). The total stock of the stand increasedby 12.5% and amounts to 4321.83 thousand m3. The increase in area occurred from 9.1% (3.06 thousandm3) in common oak to 37.7% (28.98 thousand m3) in hanging birch. The increase in the average stock per1 ha of forest vegetation is from 1.11 m3 / ha in aspens to 32.52 m3 / ha in hanging birch. This analysisof changes in land categories and average tax indicators is necessary to develop an effective action planfor forest conservation, increase the forest cover of the Reserve and provide future status of old growthforest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".