Research of an Abandoned Tailings Deposit in the Iberian Pyritic Belt: Characterization and Gross Reserves Estimation
Bibliographic record
Abstract
Global situations such as economic recovery after a pandemic, geopolitical instability, and future digital and energy transition are some of the drivers for the European Union (EU) to explore new and existing sources of raw materials. The Iberian Pyrite Belt in the southwest of the Iberian Peninsula (Spain and Portugal) hosts a great number of tailing deposits from centuries of mining operations. A unique tailings deposit has been studied and characterized. The similarities with other tailing deposits deeply studied suggested the presence of critical raw materials. Furthermore, a very gross reserves estimation was made. The characterization and reserves estimations were compared with the bibliography from mining companies who operated in the area decades ago and from the bibliography available at Fundación Riotinto. The presence of critical raw materials was confirmed, some of them in high concentrations. Moreover, a singular difference was found compared with other similar tailings stored within the Iberian Pyrite Belt. The main valuable metals identified were Au (2.25 ppm), Ag (215 ppm), Co (131 ppm) and Cu (0.29%). The reserves estimation showed that this deposit potentially hosts 1.86 t of Au, 177 t of Ag, 108 t of Co or 2358 t of Cu; in other words, with a copper average price of 8366 US$/t in December 2022, the tailings deposit contains a potential value of more than 19 million USD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".