Research on the design and optimization of ecological landscape water body network based on linear programming
Bibliographic record
Abstract
With the rapid development of the regional economy, the urbanization process is gradually accelerated, and the ecological safety problems of the urban water body network gradually appear, so this paper is based on linear planning to optimize the ecological landscape water body network.The study first gives a detailed description of the linear planning theory and highlights the gray linear planning model used.Based on the ecological constraints of the landscape pattern quantity optimization research, the "top-down" gray linear planning model from six aspects to build ecological constraints and objective function, through the simplex method to solve, resulting in different scenarios of the total amount of control of the optimization program.Three practicable optimization scenarios are obtained through repeated debugging of the optimization results, and the three scenarios achieve different results in terms of economic and ecological values.In this paper, effective optimization schemes are proposed for different optimization purposes, which on the one hand make the optimization results more realistically reflect the changes of the ecological landscape water body network, and on the other hand provide an optimal model for the management and development of the ecological landscape water body network, and promote the sustainable development of the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".