Bibliographic record
Abstract
Abstract One of the challenges of modern terrain modelling methods is to incorporate non-existing, planned features in the output. Remote sensing based solutions can only detect structures and shapes that are already present in the environment. In order to assess the impacts of a planned development on the surrounding landscape properly it is inevitable to solve this issue. In addition to the environmental, social and economic consequences, mining activities, especially open cast mining will also leave significant scars on the landscape. These can not only have a visual effect but also impact local weather conditions by changing winds, precipitation patterns. The current paper demonstrates a collection of methods and techniques able to cope with the various challenges that arise when modelling the landscape impacts of such developments. The experiments were performed in the area of Roşia Montană, where a Canadian company plans to create the largest open cast gold mine in Europe. The results of the terrain modelling process allow for the quantification of the estimated impacts on the terrain and the land cover of the area caused by the mining project. The presented methodology and visualisation tools can also facilitate the decision support mechanisms making the communication ‘more understandable’ amongst stakeholders; information meetings and public hearings involving organizing groups at any level. Obtaining the results required the development of several unconventional techniques especially in terrain modelling and visual landscape simulation, involving the combination of sometimes very different base methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".