ANALYSIS AND ASSESSMENT OF VIEWS UNDER THE CONSIDERATIONS OFHISTORICAL URBAN LANDSCAPE (HUL). CASE STUDY CUENCA, ECUADOR
Bibliographic record
Abstract
Abstract. The cities have developed over time responding to various urban dynamics, in this process have been configured representative images, product of the synergy created between the natural elements of the environment and those built by the communities. The analysis of visuals, materializes a landscape value, not evidenced at the time of planning and design projects for cities with value surroundings; you can take as an example those good practices that other countries have implemented to assess, preserve and protect views such as English Heritage (2011), London View Management Framework (2012) or View Protection Guidelines of the city of Vancouver (2011). The methodological analyzes the view in two stages: the first one strategic points of observation and view basins are identified and described as element integrators – what is seen, and through citizen participation accepts or does not accept the evaluation criteria; in the second, the view is evaluated through the relationship between quality and incidence, giving it an assessment of how fragile it is. The application of the methodology in the area known as El Ejido in the city of Cuenca – Ecuador, has resulted in a total of twentyeight visuals considered relevant. Nine of them, have been analyzed completely, evidencing that there is a view quality very High / High; nevertheless, they are affected by urban actions that generate that the incidence is High and therefore the fragility and vulnerability is greater.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".