Igneous Rock Associations 20. Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes
Bibliographic record
Abstract
It has been nearly fifty years since Tom Pearce devised a type of element ratio diagram that isolates the effects of crystal fractionation and accumulation (sorting) hidden in the chemistry of a suite of igneous rocks. Here, we review the guiding principles and methods supporting the Pearce element ratio paradigm and provide worked examples with data from the Mauna Ulu lava flows (erupted 1970–1971, Kilauea Volcano, Hawaii). Construction of Pearce element ratio diagrams requires minimum data; a single rock analysis can suffice. The remaining data test the model. If the data fit the model, then the model is accepted as a plausible or likely explanation for the observed chemical variations. If the data do not fit, the model is rejected. Successful applications of Pearce element ratios require the presence and identification of conserved elements; elements that remain in the melt during the processes causing the chemical diversity. Conserved elements are identified through a priori knowledge of the physical-chemical behaviour of the elements in rock-forming processes, plots of weight percentages of pairs of oxides against each other, or by constant ratios of two elements. Three kinds of Pearce element ratio diagrams comprise a model: conserved element, assemblage test, and phase discrimination diagrams. The axial ratios for Pearce ratio diagrams are combinations of elements chosen on the basis of the chemical stoichiometry embedded in the model. Matrix algebra, operating on mineral formulae and analyses, is used to calculate the axis ratios. Models are verified by substituting element numbers from mineral formulae into the ratios. Different intercepts of trends on Pearce element ratio diagrams distinguish different magma batches and, by inference, different melting events. We show that the Mauna Ulu magmas derive from two distinct batches, modified by sorting of olivine, clinopyroxene, plagioclase and, possibly, orthopyroxene (unobserved).RÉSUMÉIl y a près de cinquante ans Tom Pearce a conçu un genre de diagramme de ratio d’éléments qui permet d’isoler les effets de la cristallisation fractionnée et de l'accumulation cristalline (tri) au sein de la chimie d'une suite de roches ignées. Dans le présent article, nous passons en revue les principes et les méthodes étayant le paradigme de ratio d’éléments de Pearce, et présentons des exemples pratiques à partir de données provenant de coulées de lave du Mauna Ulu (éruption 1970–1971 du volcan Kilauea, Hawaii). La confection des diagrammes de ratio d’éléments de Pearce requière un minimum de données; une seule analyse de roche peut suffire. Les données restantes servent à tester le modèle. Si les données sont conformes au modèle, alors le modèle est accepté comme explication plausible ou probable des variations chimiques observées. Si les données ne correspondent pas, le modèle est rejeté. Les applications réussies des ratios d’éléments de Pearce requièrent la présence et l'identification d’éléments conservés; éléments qui demeurent dans la masse fondue au cours des processus causant la diversité chimique. Les éléments conservés sont identifiés par la connaissance a priori du comportement physico-chimique des éléments dans les processus de formation des roches, le positionnement sur la courbe des pourcentages pondérés de pairs d'oxydes les uns contre les autres, ou par des ratios constants de deux éléments. Trois types de diagrammes de Pearce de ratio d’éléments constituent un modèle: élément conservé, test d'assemblage, et diagrammes de phase discriminant. Les ratios axiaux pour les diagrammes de ratio d’éléments de Pearce sont des combinaisons d'éléments choisis sur la base de la stœchiométrie inhérente au modèle. L’algèbre matricielle, appliquée à des formules minérales et à des analyses, est utilisée pour calculer les ratios axiaux. Les modèles sont vérifiés en utilisant les nombres d’élément des formules minérales dans les ratios. Différentes intersections dans les diagrammes de ratios d’éléments de Pearce distinguent différents lots de magma et, par inférence, différentes coulées. Nous montrons que les magmas de Mauna Ulu proviennent de deux lots distincts, modifiés par l’extraction de l'olivine, de clinopyroxène, de plagioclase et, éventuellement, orthopyroxène (non observé).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".