Compilation of Mineral Resource Data for Mississippi Valley-Type and Clastic-Dominated Sediment-Hosted Lead-Zinc Deposits
Bibliographic record
Abstract
This report contains a global compilation of the mineral resource data for sediment-hosted lead-zinc (SH Pb-Zn) deposits. Sediment-hosted lead-zinc deposits are historically the most significant sources of lead and zinc, and are mined throughout the world. The most important SH Pb-Zn deposits are hosted in clastic-dominated sedimentary rock sequences (CD Pb-Zn) that are traditionally called sedimentary exhalative (SEDEX) deposits, and those in carbonate-dominated sequences that are known as Mississippi Valley-type (MVT) Pb-Zn deposits. In this report, we do not include sandstone-Pb, sandstone-hosted Pb, or Pb-Zn vein districts such as those in Freiberg, Germany, or Coeur d'Alene, Idaho, because these deposits probably represent different deposit types (Leach and others, 2005). We do not include fracture-controlled deposits in which fluorite is dominant and barite typically abundant (for example, Central Kentucky; Hansonburg, N. Mex.) or the stratabound fluorite-rich, but also lead- and zinc-bearing deposits, such as those in southern Illinois, which are considered a genetic variant of carbonate-hosted Pb-Zn deposits (Leach and Sangster, 1993). This report updates the Pb, Zn, copper (Cu), and silver (Ag) grade and tonnage data in Leach and others (2005), which itself was based on efforts in the Canadian Geological Survey World Minerals Geoscience Database Project (contributions of D.F. Sangster to Sinclair and others, 1999). New geological or geochronological data, classifications of the tectonic environment in which the deposits formed, and key references to the geology of the deposits are presented in our report. Data for 121 CD deposits, 113 MVT deposits, and 6 unclassified deposits that were previously classified as either SEDEX or MVT in the Leach and others (2005) compilation, are given in appendix table A1. In some cases, mineral resource data were available only for total district resources, but not for individual mines within the district. For these districts, the resource data are presented in appendix table A2. In addition, numerous figures (appendix figures B1-B9) displaying important grade-tonnage and geologic features are included. These mineral deposit resource data are important for exploration targeting and mineral resource assessments. There is significant variability in the resource data for these deposit types, and ore controls vary from one region to another. Therefore, grade-tonnage estimations are best evaluated as subsets of the data in appendix table A1 where local mineralization styles and ore controls characterize the region being evaluated for grade-tonnage relations. Furthermore, consideration should also be given to the tendency for MVT resources to occur in large mineralized regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".