PLANNING RELOCATION OF PEOPLE FOR DEVELOPING SURFACE MINES IN DENSELY POPULATED AREAS: OPTIMIZATION OF MULTIPLE OBJECTIVES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Surface mining, often adopted for exploiting natural resources all over the world, is a major subject of debate as it causes major environmental impacts. It not only adversely alters the landscape but it also seriously hampers the traditional living conditions of numerous inhabitants, who may be displaced against their wishes without receiving necessary compensation. In this paper, goal programming is combined with the analytic hierarchy process to determine optimal decisions for the planned relocation of people where surface mining may take place in a densely populated environment, while addressing multiple conflicting objectives. The combined approach is illustrated with a numerical example highlighting its usage in other decision problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it