Byproduct-to-Host Ratios for Assessing the Accessibility of Mineral Resources
Bibliographic record
Abstract
Mineral resources are essential for reaching net-zero ambitions by 2050. There is a rising diversity of metals in electricity generation and storage technologies, as well as for mobility technologies. However, little is known about the future supply of minor elements historically mined in low volumes such as indium, tellurium, germanium, or tantalum. Those minor elements are found in lower concentrations in the ores of major elements and therefore rarely form economic deposits on their own. Such elements are often produced as byproducts of a host (or "target commodity", which underpins the bulk of a mine's profitability) in ore, e.g., in porphyry ore, tellurium is a byproduct where copper is the host. As a result, the primary supply of those minor elements depends on the supply of the major elements. Such dependency has not been accounted for in scenarios of the mineral supply. To address this gap, we developed a methodology to harmonize scattered data of mineral resource estimates and to calculate the mass ratio between the byproduct and the host in ores and concentrates, called the byproduct-to-host (BtH) ratio. We collected crude ore tonnage and element grades, among other key data, from the state-of-the-art literature and publicly available mining company reports. Our data set covers 3422 deposits across 141 countries providing 22 275 BtH ratios. The future supply of minor elements can be derived by multiplying the primary production of host elements by the developed BtH ratios, noting the limitations of data representativity. The open-access nature of this work facilitates the enrichment and update of this data set in the coming years.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".