Performance Evaluation on the Polices of Cultivated Land Protection Based on the Entire-Array-Polygon Indicator Method—Taking Gansu Province as an Example
Bibliographic record
Abstract
The purpose of the research is to evaluate operational effect of cultivated land protection polices with objective and reasonable by diagnosing problems,constructing indicators and examining model. The research methods are literature review and mathematical model. The research confirmed that the overall level of policies performance of cultivated land protection in Gansu Province is lower with level IV during2005-2012,and it also has features of fluctuate change,slowly rising and improving year by year. The indexes of quantity,quality and input tend to steady or rising in fluctuation,the ecological protection indicators show a downward trend year by year,and the implementation effect of policies executive power is not well during the evaluation period. The research concluded that cultivated land protection is a long-team work,and the effect of protection need an amount of time to accumulate. Cultivated land protection in Gansu Province focus more on element input and quantity directions. The entire-array-polygon indicator method which introduced to evaluation is convenient and reasonable,the results of evaluation is objective and comprehensive,and it also an ideal method which used to performance evaluation on the policies of cultivated land protection.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".