Forest health evaluation for tending of recreational forest in Xishan Forest Farm in Beijing city
Bibliographic record
Abstract
Confronted with the problems of decline in quality,high density,poorly natural pruning and high fire danger rating of the forest landscape in Xishan Forest Farm in Beijing city,the measures were taken for each forest stand such as ecological thinning,landscape thinning,pruning,combustible management,plants singling and complementary replanting by combining the relevant theory and technology of forest health,and the effects of tending techniques on forest health management were evaluated;for better forest health,the reserved tending density was 1375~1975 trees·hm-2 for Oriental arborvitae,550~700 trees·hm-2 for Robinia pseudoacacia,about 1100 trees·hm-2 for Smoke tree,about 900 trees·hm-2 for Chinese pine and about 1588 trees·hm-2 for Acer truncatum;Every Forest Health Comprehensive index(HCI) has been enhanced after tending with O.arborvitae increasing by 31.27%,R.pseudoacacia by 22.22%,Smoke tree by 11.11%,Chinese pine by 15.00% and Acer truncatum by 17.65%.It is obvious that the forest health quality of each forest stand was significantly improved,which provides new operation standards and ideas for rational development and utilization of the scenic and recreational forest resources in Xishan Forest Farm in Beijing city,at the same time,also provides theoretical and technical references for the health operation of the forest stand with similar functions and problems in different areas.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".