The Calculation and Use of Sulfide Metal Contents in the Study of Magmatic Ore Deposits: A Methodological Analysis
Bibliographic record
Abstract
Research Article| October 01, 2001 The Calculation and Use of Sulfide Metal Contents in the Study of Magmatic Ore Deposits: A Methodological Analysis ANDREW KERR ANDREW KERR Geological Survey of Newfoundland and Labrador, Department of Mines and Energy, St. John's, Newfoundland, Canada, A1B 4J6 Search for other works by this author on: GSW Google Scholar Exploration and Mining Geology (2001) 10 (4): 289–301. https://doi.org/10.2113/0100289 Article history received: 05 Sep 2002 accepted: 22 Jan 2003 first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation ANDREW KERR; The Calculation and Use of Sulfide Metal Contents in the Study of Magmatic Ore Deposits: A Methodological Analysis. Exploration and Mining Geology 2001;; 10 (4): 289–301. doi: https://doi.org/10.2113/0100289 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyExploration and Mining Geology Search Advanced Search Abstract The base-metal and PGE contents of samples from magmatic sulfide mineralization are commonly correlated with their sulfide contents, indicating that the metal contents of bulk sulfides remain approximately constant within a given prospect or part thereof. Calculated sulfide metal contents provide valuable information in mineral exploration and research, but there are few formal descriptions and analyses of the procedures. Sulfide metal contents are best calculated using an assumed value (35.7% S) for a typical pyrrhotite-chalcopyrite-pentlandite mixture, and there appears to be little advantage in accounting for sulfide species separately. Regression of metal data against sulfur is probably the most rigorous approach, but is not always practical. Above 10% S, calculations are very robust, but lower sulfide contents generally demand at least some correction for non-sulfide-hosted metals. Such corrections can become significant below 5% S, and/or in olivine-rich samples. They are best accomplished by mass-balance calculations, using concentration data from unmineralized host rocks. Significant uncertainties are introduced by analytical errors for sulfur, base-metals, and PGE, which are commonly measured from separate sample aliquots. These combined errors in sulfide metal contents generally exceed ±10%, but expand further at low S contents. In general, treatment of data from samples containing <2.5% S must be approached with caution, especially for PGE, for which the exact host minerals may not be known. Application of the method in simple grade-potential assessment is straightforward, but research studies involving sulfide-poor samples are inherently more complex. Under-correction or over-correction of data for non-sulfide-hosted metals can lead to false negative or positive correlations between sulfide metal contents and sulfide content. As the latter may itself be linked to geological parameters, such as depth within an intrusive body, undue significance could be ascribed to such trends. There are also valid geological reasons for such correlations, and such data require careful assessment to separate true and artificial variations. Propagated analytical uncertainties increase significantly in sulfide-poor samples, and must also be borne in mind whenever data from different localities or units are compared and contrasted. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.275 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".