Iron ore extract by the mine method: regression model of an ecological backpack
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".