Green Infrastructure.A math model for optimizing economic and sustainable public investments in the urban area
Bibliographic record
Abstract
In this paper we present a methodology for analyzing the system of green/gray infrastructure urban environments classified for their environmental sustainability. This analysis, allows the construction of a matrix that can be analyzed mathematically. The knapsack problem of multiple choice is the basis of the approach is proposed where a resolutive efficient algorithm to obtain the optimal solution to the problem linear, and this algorithmic method is incorporated in a dynamic programming algorithm for the entire problem. In the case treated the second objective function has been used to minimize the overall difference between the states of competence of each infrastructure. A further set of constraints has been used in the two macroclasses containing respectively the infrastructure type of public and private type. The model used in this work was developed ad hoc to represent the decision problem under consideration and all its characteristics. The model developed thus presents elements of originality, to the best knowledge of the author. To improve the acceptability of the design choices is also introduced the process of participatory planning through the electronic town meeting. The results of this methodological approach optimizes the use of limited financial resources towards a better quality of life in urban environments.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".