Mitigating the Impact of Factories on the Landscape: An Assessment and Design Support Tool
Bibliographic record
Abstract
Being widely responsible for environmental degradation, industry represents a key asset to manage for a more sustainable and healthier living environment.However, factories affect more than just the physical sphere of the landscape, also impacting social and economic spheres as their intense perceptual-aesthetic interferences with the scenery can disturb neighbors and damage corporate images.In the recent past, policy-makers, practitioners and communities have demonstrated that the harmonization of industry with the landscape can produce several positive effects.In this framework, multicriteria systems to assess the impact have been developed, but they are mainly focused on reducing negative environmental effects rather than perceptual ones, while a holistic approach appears to be needed.Therefore, a method of analyzing how facilities interfere with the landscape is proposed, along with the development of a set of strategies to lessen detrimental effects on the physical, perceptual-aesthetic and social/cultural dimensions of the landscape.This paper presents the main outputs of the research, including the structure of the assessment system and a catalog of case studies selected for good design practices, from which general mitigation tactics have been retrieved.The result is a protocol composed of an assessment system and a design support tool available to companies and designers to be inspired by.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".