Dynamic Matrix for an Adaptive Environment Management in Mining: A Feed-engineering Alternative?
Bibliographic record
Abstract
Environment impacts are usually determined by quantification or an evaluation system derived from several methodologies including environmental assessment, matrices, and data cross-referencing. This study uses a dataset obtained from validated mining Environmental Impact Assessments (EIAs), some monitoring reports and scientific insights on open-pit mines (OPM). The purpose here is to build a dynamic matrix system over time to facilitate a systemic evaluation of environmental impacts and to find in-depth preventive measures in any OPM. The four dynamic matrices are built with qualitative and numerical values in both magnitude and significance terms. As one of the issues is to minimize negative risks in OPMs, one outcome points out the environmental factors of mining operations sensitive to the variations over time and the variability of the parameters themselves. The results show secondly that the data (qualitative and quantitative) vary from EIA stage to a post EIA status like activities or environmental factors numbers. Thirdly, the impact of activities on each part of environment components and the incidence of all activities during the mines’ life cycle is easier to identify whatever the data density. In the fourth line, this paper indicates that the dynamic matrix in an optimal alternative in the process of determining preventive measures to mitigate the risks and the need for an interactive environmental follow-up program in mining or similar industry. This approach reduces the following-up monitoring weaknesses and allows managers, as a multi-criterion decision-making approach, to take enlightened actions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".