IT And Systemic Approach For Managing Sustainable Urban Infrastructure Rehabilitation
Bibliographic record
Abstract
Urban development sustainability calls for new approaches involving economic, social political and environmental dimensions in order to face the institutional complexity of decision-making. That situation imposes increasingly growing constrains and challenges upon administrations in large cities. Therefore the decision to rehabilitate a particular asset in a strongly urbanize area is a complex task witch depends on many parameters. Often lack of relevant information during infrastructures life cycle results in imprecise definition of what has to be done creating unexpected changes and costly impacts. This paper presents a general approach develop base on some work implemented for urban s network rehabilitation. In Verdun by the use of total quality management and knowledge management the project team adopted a systemic approach and developed an integrated management system to manage project information flows, mobilized the internal human resources, managed the dynamics of the process, reduced uncertainties to solved problems. The propose methodology allows manager to control information feedback loops to manage budget, program, conformance, risk and uncertainties. Development of a conceptual framework is described. The model enables managers to use IT and monitoring equipments to handle information, resource, material and people flows in the most efficient manner to generate add value to a rehabilitated asset and to maximize results integrities during planning and execution phases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".