Bibliographic record
Abstract
There is a need to restore the terrain back its natural environment after mining development. It is necessary to compare the original and developing surfaces for post-management and to analyze the terrain change to develop a process for efficient restoration plan. This study analyzes and compares change to the terrain by annual mining development using GIS. Contours digitized with CAD based on photogrammetry are classified into annual data and created by Triangulated Irregular Network (TIN). By producing profiles and cross sections using TIN, many stations are distinguished. As a result of the terrain changes caused by mining development from 2000 to 2003 by operating elevation values each cell converted to raster from TIN, are cut and are filled up to 46% of cut volume, and annual surface changes of cut and fill area to mining are analyzed to visual and quantitative data. This study is used for the restoration plan and additional mining. And it is expected that this annual change, caused by mining development, can be used to return the terrain close to its original condition for finished mining area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".