Landscape aesthetic and visual analysis facing the challenge of development of sustainable landscapes – a case study of the post-industrial area to the left margin of the Arade River
Bibliographic record
Abstract
It is increasingly recognized that growing demands for diverse, multifunctional and quality recreation landscapes have placed a high pressure on the natural resources and its management.This fact coupled with the current common way of perceiving landscape as something separate from us, with several functions for society and individuals -in spite of something that changes automatically according to cultural, economic and social changes, enhances the necessity for developing new approaches and methodologies that effectively apply sustainable principles to landscape monitoring, planning and management.For this reason, adequate information about the existing landscape and about the nature of places that it is desirable to make cannot be the result of superfi cial approaches based exclusively on designers and planners' ideas.Even if planning and monitoring programs frequently use remote sensing data and focus only on changes in land cover and land use in relation to values such as biodiversity, land capability and recreation, they often neglect landscape aesthetics, cultural heritage and public will.To show that it is possible to combine all these factors in a specifi c approach, this paper presents a specifi c study of the estuarine landscape of the Arade River in Algarve, Portugal, based on a methodology that incorporates SWOT (strengths, weaknesses, opportunities and threats) analysis and aesthetic, cultural, biophysical and social factors in landscape assessment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".