Dynamic assessment of forest resources quality at the provincial level using AHP and cluster analysis
Bibliographic record
Abstract
The aim of this study was to quantitatively assess and analyze the dynamic changes and current problems of Chinese forest resources based on the National Forest Inventory (NFI). In this study, a hierarchical model was established using the analytic hierarchy process to assess forest resources quality (FRQ) at the provincial level. Four criteria were used, including forest quantity, forest productivity, forest structure, and forest health and each criterion was further composed of multiple factors. Among these assessment factors, stock volume per unit area was the most important, while canopy structure was the least important. The ranges of FRQ Indies across China during the 6th NFI (1999–2003), 7th NFI (2004–2008), and 8th NFI (2009–2013) were 0.3031–0.6366, 0.3499–0.7186, and 0.3534–0.7555, respectively. From the 6th to 8th NFI, forest quality improved by different degrees for all provinces, whereas the other three criteria presented an increasing or decreasing trend. In general, the implementation of ecological projects has significantly improved the FRQ at provincial and national levels. During the 8th NFI, the FRQ levels were excellent for 3 provinces, good for 15 provinces, medium for 12 provinces, and only one province exhibited an inferior level of FRQ. Based on cluster analysis, Chinese forest resources during the 8th NFI could be grouped into four clusters according to the provincial administrative region, and each cluster had its own advantages and disadvantages. Stock volume increment and forest calamity were in a very good state, while canopy structure was the key factor limiting the FQR for all the clusters. Some relevant measures were proposed to improve the existing conditions of Chinese forest resources. The results of this study are significant in that they can provide theoretical and technical references for future adjustment and sustainable management of forest resources in China.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".