Research on Optimization of Rural Characteristic Industry Development Path Based on Multi-Objective Planning in the Context of Rural Revitalization
Bibliographic record
Abstract
Encouraged by the strategy of rural revitalization, rural areas in many places are exploring the development path of characteristic industries.The article embeds the multi-objective optimization model into the development of rural characteristic industries to optimize the current rural industrial development path.The multi-objective optimization model of rural characteristic industry development is constructed, and the ACO-PSO algorithm is used to solve the model, in order to realize the organic unity of economic, social and ecological benefits of rural industry development.The multiobjective optimization model is used to optimize the industrial development of village S. The total regional output value of village S in 2035 is 2.08 times of that in 2025.The proportion of output value of primary industry and secondary industry decreases by 20.29% and 18.50% respectively.The proportion of tertiary industry output value increases by 38.79%, and the industrial structure becomes more and more reasonable.After the multi-objective optimization, Village S changes the development mode at the expense of resources and the environment, and maintains the survival of the ecological environment by appropriately slowing down the economic development.After the multiobjective optimization, the total output value of the primary industry and the per capita income of farmers in Village S increased by 17,412 and 205.76 yuan respectively.The total output of tourism in the tertiary industry is 465,222,000 yuan, which is 126% higher than that before optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".