Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure
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
Activity in the mining industry is based on the profitability principle similar to other business sectors. In the case of stone pits, gravel and sand quarries, it presents a very complex task, mainly due to the fact that the economy of localities is influenced greatly by natural conditions, which cannot be changed. The presented contribution deals with the problem of how mining companies, realizing the surface extraction of construction materials, could be profitable in the future. The main research method of this contribution presents regression and correlation analyses with the goal of determining parameters with a decisive influence on the future economic development of the locality. A complex system of stone pit, gravel and sand quarries demanded discriminant analysis to evaluate individual localities with the goal of dividing them into profitable and not profitable localities. The results of the contribution divide localities of quarry mining among profitable or not profitable, serving for predicting the future development of the company, based on discriminant analysis. The results of maximally possible measures respect assumptions, enabling the correct application of such multivariate statistical methods. A further orientation of the research in an area of model creation for predicting the future development of the company is possible in the application of logistic regression and neuron nets.
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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 | 0.001 | 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 | 0.000 |
| 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 it