Critical Raw Materials (CRMs) investigation in dismissedhistorical Volcanogenic Massive Sulfide (VMS) depositsin the Northern Apennine (Italy)
Bibliographic record
Abstract
Northern Italy has more than 100 abandoned mining sites, most of which are now under investigation to determine their Critical Raw Materials (CRMs, i.e., minerals or elements whose supply is not enough to satisfy the global demand) content [Kiss et al., 2023]. Dismissed mining sites in the Emilia Romagna region are no exception, in particular, the Boccassuolo mines, related to ophiolitic terrains (Boccassuolo Ophiolite). These sites are receiving new interest due to the various disseminated historical extractive spots of Cyprus-type Volcanogenic Massive Sulfide deposits [Garuti et al., 2011]. These deposits, also known as VMS, occur here as pods within small bodies of ophiolitic basalts cropping out as olistoliths in the Northern Apennine External Ligurian units and owe their origin to metal-rich hydrothermal fluid circulation which developed quartz-sulfide veins when mixed with seawater through a fissures network [Saccani, 2015]. They are characterized by sequences of pillow lavas associated with serpentine and gabbro breccias, radiolarian cherts, limestones, and abundant serpentinized subcontinental mantle peridotites [Kiss et al., 2023]. In this area, basalts show OCTZ chemical features with transitional-MORB affinity and a garnet signature (Dyn/Yb0=1.2-1.4). Bulk rock geochemical analyses for major and trace elements were performed, and preliminary results agree with previous studies [e.g., Barrie & Hannington, 1999, Zaccarini & Garuti, 2008]: these deposits are Cu and Cu-Zn VMS types, with almost no Pb (<1 ppm), with Cu max. 200*UCC, Zn max. 118*UCC, and Ag max. 12*UCC [Rudnick & Gao, 2014]. Such preliminary data provide the first relevant information to map trace metal enrichment distribution in the main rocks. Radiogenic (Sr-Nd-Pb) and stable (S-C) isotopic analyses and another sampling in Montecreto and other sites in the Emilia Romagna region are planned to compare the results, increase the available database, and estimate the CRMs volumes. References Barrie C.T., and Hannington M.D., (1999). Introduction: Classification of Volcanic‐Associated Massive Sulfide Deposits Based on Host‐Rock Composition. Volcanic-Associated Massive Sulfide Deposits: Processes and Examples in Modern and Ancient Settings, 8, 2-10. doi:10.5382/Rev.08.01 Garuti G., Zaccarini F., Scacchetti M., and Bartoli O., (2011). The Pb‐rich sulfide veins in the Boccassuolo ophiolite: Implications for the geochemical evolution of hydrothermal activity across the ocean‐continent transition in the Ligurian Tethys (Northern‐Apennine, Italy). Lithos, 124(3- 4), 243-254. https://doi.org/10.1016/j.lithos.2010.11.006 Kiss G.B., Molnár K., Benkó Z., Skoda P., Kapui Z., Garuti G., Zaccarini F., Palcsu L. and Czuppon G., (2023). Tracing the Source of Hydrothermal Fluid in Ophiolite‐Related Volcanogenic Massive Sulfide Deposits: A Case Study from the Italian Northern Apennines. Minerals, 13(1)(8). https://doi.org/10.3390/min13010008 Rudnick R.L., and Gao S., (2014). Composition of the Continental Crust. Treatise on Geochemistry, 1-51. https://doi.org/10.1016/B978-0-08-095975-7.00301-6 Saccani E., (2015). A new method of discriminating different types of post‐Archean ophiolitic basalts Abstract Volume 2nd SOGEI Conference July 1-4, 2024 | Perugia, Italy 76 and their tectonic significance using Th‐Nb and Ce‐Dy‐Yb systematics. Geoscience Frontiers, 6(4), 481-501. https://doi.org/10.1016/j.gsf.2014.03.006 Zaccarini F., and Garuti G., (2008). Mineralogy and chemical composition of VMS deposits. Mineralogy and Petrology, 94, 61-83. https://doi.org/10.1007/s00710-008-0010-9
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 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".