An RP-MCE-SOP Framework for China’s County-Level “Three-Space” and “Three-Line” Planning—An Integration of Rational Planning, Multi-Criteria Evaluation, and Spatial Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
“Three-space” (including agricultural space, urban and rural construction space, and ecological space) and “three-line” (including urban development boundary, prime farmland control line, basic ecological control line) planning has been regarded as an essential measure for China’s city and county level “multiple-plan integration”. It handles the multiple planning objectives of development management, agricultural land preservation, and ecological resource protection. This article proposes a rational planning with multi-criteria evaluation and spatial optimization (RP-MCE-SOP) framework for China’s county-level “three-space” and “three-line” planning by following the rational planning (RP) model and taking advantages of multi-criteria evaluation (MCE) and spatial optimization (SOP) techniques. The framework includes five steps of building the SOP model, land suitability evaluation with MCE, optimization problem solving, post-processing of land allocation solutions, and applying post-processed solutions to “three-space” and “three-line” planning. The framework was implemented in Dongxihu District of Wuhan City with the Boolean aggregation and analytical hierarchy analysis (AHP) MCE techniques and the patch-based Non-dominated Genetic Algorithm (NSGA-II) SOP algorithm. The case study shows: (1) The framework is feasible and useful for assisting decision making in “three-space” and “three-line” planning. (2) The planning solutions protect ecologically sensitive spaces and high-quality agricultural land and plan future construction in the urban peripheral area or transportation convenient areas. (3) The solutions are useful for planning the hard boundaries for ecological resource protection and prime farmland preservation and setting both hard and soft boundaries for urban growth.
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.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it