Iron ore extract by the mine method: regression model of an ecological backpack
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
The object of research: the “backpack factors”, which are five products: biotic materials; abiotic materials; water; air; soil that has been moved. Investigated problem: to develop a regression model of an ecological backpack that considers the statistical significance of factors for Ukrainian iron ore mining enterprises. The main scientific results: by experimental investigations were conducted following a 2(5-2) matrix plan, consisting of 8 experiments, was determined that 4 factors are statistically significant, excluding the first factor, biotic materials. The most substantial influence on the response function is attributed to air, which includes both mine ventilation flows and compressed air used during iron ore mining. Water represents the second most influential factor, followed by the volume of displaced rock, and finally, abiotic factors, particularly electricity and fuel. Hence, iron ore mining operations essentially function as air processing and water disposal enterprises, highlighting their prominence within this specific domain. The area of practical use of the research results: In line with the principles of the case method, our research is conducted using a real operational enterprise Sukha Balka PJSC located in the city of Kryvyi Rih, Ukraine. Data collection is achieved through a method of multi-year monitoring of the company's activities spanning from 2000 to 2021, which forms the basis for our case study. In the future, it would be prudent to develop ecological backpack models tailored to open-pit iron ore mining enterprises. Innovative technological product: Calculating MIpS 2.0 from Material Flow Analys (MFA) field. Scope of the innovative technological product: The obtained regression relationship enables the prediction of the ecological backpack's fullness based on input factor values.
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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 | 0.000 | 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