Landscape changes due to quarrying activities as a project parameter for urban planning
Bibliographic record
Abstract
This article presents a procedure to analyse the consequences induced by extractive activities on the surrounding landscape.The objective is to predict the extent of visual interference a given extractive venture will have on the landscape, while taking into account the landscape sensitivity of the area.To this aim, a method is proposed for evaluating the relationship between extractive methods and resultant visual impact by means of a case study in the Lazio region of Italy.Having determined the site for the extractive activities, an annual production target is fixed.In relation to the type of material to be extracted, various options are then selected according to the possible extraction methods and, for each of these, quantitative indicators associated with the resulting visual impact are determined and evaluated.The landscape sensitivity of the area surrounding the site is considered to evaluate the possible effect on the various types of observers who may be present.The procedure described in this article constitutes a concise instrument to be used as a decision-making aid during the planning stage of a quarrying or mining venture.It would equally be of help to the regulatory authorities and to any property developers involved in making building choices, which would be affected by nearby extractive plants or any large construction work in general.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".