OVERVIEW ON LCA: CHALLENGES AND OPPORTUNITIES FOR THE REFRACTORY INDUSTRY
Bibliographic record
Abstract
Organizations are now integrating life cycle thinking tools and techniques into decision-making to enable an analysis of the environmental impacts associated with all stages of a product’s life. In this context, mining has been questioned by the widespread consensus that reducing resource consumption is a requirement for sustainable development. On the other side, it is clear that, due to dissipation, virgin raw materials will always be needed, and that circular economy thinking should integrate the mining industry and not oppose it. From this emerges the concern of resource depletion and the abiotic depletion potential (ADP) comes therefore as an attempt to assess the risk of depletion within life cycle assessment (LCA) methodology. However, when it comes to mineral resources, a lot of general assumptions are made, and the specificities of each element are often neglected. By attempting to include anthropogenic stocks in the calculations, some authors also neglect the singularities of each product. LCA has proven to be a powerful tool and its successful application within the refractory industry depends on collaboration between organizations in order to fill the numerous lacks of data availability and to overcome the challenges ahead. This paper is part of a PhD project that aims to build a database of magnesia production, from the mine to the kiln, to support LCA as well as to discuss resource depletion within the methodology and to account for the benefits and challenges of refractory recycling, focusing on magnesia bricks.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".