Basis for a New Life Cycle Inventory for Metals from Mine Tailings Using a Conceptual Model Tool
Bibliographic record
Abstract
Life Cycle Impact Assessments (LCIAs) examine the environmental impacts of products using life cycle inventories (LCIs) of quantified inputs and outputs of a product through its life cycle. Currently, estimated impacts from mining are dominated by long-term metal release from tailings due to inaccurate assumptions regarding metal release and transport within and from mine materials. A conceptual model approach is proposed to support the development of a new database of LCI data, applying mechanistic processes required for the release and transport of metals through tailings and categorizing model inputs into ‘bins’. The binning approach argues for accuracy over precision, noting that precise metal release rates are likely impossible with the often-limited data available. Three case studies show the range of forecasted metal release rates, where even after decades of monitoring within the tailings and underlying aquifer, metal release rates span several orders of magnitude (<100 mg/L to >100,000 mg/L sulfate at the Faro Mine). The proposed tool may be useful for the development of a new database of LCI data, as well as to analyze mine’s regional considerations during designs for risk evaluation, management and control prior to development, when data is also scarce.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".