How much metal can you get? Quantified mass balancing of base metal release during epidosite zone alteration in ophiolite-hosted VMS systems
Bibliographic record
Abstract
Understanding source-deposit relationships in VMS systems is important for mineral exploration and to increase knowledge of seafloor hydrothermal processes and ocean–crust fluxes. Although it is known that metals are stripped from oceanic crust by hydrothermal fluids and are partly redeposited in orebodies, some aspects are poorly understood. It has been proposed that metal-depleted epidosites (epidote–quartz–chlorite–Fe-oxide–titanite units within sheeted dyke complexes) were the source rocks for ophiolite-hosted VMS deposits. To test this hypothesis, the Spilia-Kannavia epidosite zone in the Troodos ophiolite, Cyprus, was investigated. This zone (≥1.9 km3), at the base of the Sheeted Dyke Complex, extends ~5 km parallel to and ~2 km across dyke strike and vertically ≥400 m. During alteration, this zone released ~0.4 Mt Zn, ~0.06 Mt Ni, ~5.2 Mt MnO, and 0.14 Mt Cu. This exceeds the Zn within any ophiolite-hosted VMS deposit, and provides enough Cu for a medium-large deposit. In comparison, published data suggest a similar volume of background diabase could mobilise less Zn, Cu and Ni and no Co or Mn. It is concluded that epidosite zone formation releases significant amounts of base metals into ophiolite-hosted VMS systems
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".