Impact of land conversion on rural systems in typical agricultural counties ofeastern plain area, China
Bibliographic record
Abstract
Revealing the relationship between land conversion and rural development is necessary for the healthy and integrated development of urban and rural areas. Taking two typical agricultural counties in the eastern plain area of Shandong Province, China as study cases, this article dissects the impact of land conversion on rural systems through the analysis of evolution of land conversion and rural system at different socioeconomic development stages. The results show that:(1) with the change of county economic development from low to advanced stage, the scale and ratio of land conversion increased, while the overall development trend of the rural systems was positive, with increasing comprehensive rural development index(E) values from 0.295 to 0.798 and 0.197 to 0.700 between 2000 and 2008 in Yucheng City(county-level city) and Huantai County, respectively, with fluctuations in some years;(2) the extent of impact of land conversion on rural system changed from weak to strong gradually, and the increased ratio of land conversion led to dramatic changes of some elements of the rural systems. Among these, the sensitivity of the rural economic and social subsystems to land conversion was relatively high compared to the environmental and resource subsystem. The focus of controlling land conversion and rural transformation in plain agricultural area is to build the protection mechanism for rural population transfer and the economies of scale of farmland management, regulate land use planning and management, and improve the capacity of influence of counties
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".