Evaluating Land Use Intensification Based on Entropy Method --- A Case Study of Hefei
Bibliographic record
Abstract
In this paper, we establish the index system of urban land intensive use evaluation from four aspects: land investment level, land utilization level, land output effect, land use ecological benefits. The entropy method is used to calculate indexes which measure the land intensive utilization in Hefei. According to the comprehensive evaluation model we can draw a conclusion that although the land intensive use level of Hefei was ascending from 2004 to 2008, it is still at low level in 2008. Finally, we make suggestions about how to improve the intensive use level in Hefei with the help of the integrated assessment value of each rule layer. The conclusion indicate that Hefei should solve land utilization problems by setting the target for land intensive utilization, optimizing the land use structure, improving the urban environment, enhancing the land use level, changing the direction of land utilization, only in theses ways the land utilization of Hefei will be intensive.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".