Bibliographic record
Abstract
Land development rights (LDR), meaning the rights to develop land, are a sort of property rights, derived and divided from land ownership, which the thought and use were originated from England. The law systems of LDR have been differently established in many countries and regions, such as USA, France, German, Italy, Canada, Korea and Taiwan of China, but in the Mainland of China, the law system of land development rights has not been established yet. A land use planning, as an important measure to control the use of land, is a scientific and legal base, and an important technical standard for LDR, and at the same time, its formulation and implementation depend on the clearing and limiting of LDR. During the research on the issues related with land and during the land administration, there exist many tough problems to settle, such as short of economic measures in protecting farmland and basic farmland, how to transform land use rights of collective-owned building land, etc. It will get easier to settle above-mentioned problems with the thought of LDR. So, it is very necessary to design a system of LDR, which is greatly important to further clear the ownership rights of land development, to further increase the efficient of land development, and to further protect farmland and improve the ecological environment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".