The Calculation and Use of Sulfide Metal Contents in the Study of Magmatic Ore Deposits: A Methodological Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,275 | 0,406 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,003 | 0,007 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».