Byproduct-to-Host Ratios for Assessing the Accessibility of Mineral Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it