Forest health evaluation for tending of recreational forest in Xishan Forest Farm in Beijing city
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it