Pearce Element Ratio Diagrams: Linking Geochemical Data to Magmatic Processes
Bibliographic record
Abstract
SUMMARY Pearce element ratios and diagrams (Pearce, 1968) are models designed to isolate the effects of crystal fractionation and accumulation (sorting) within 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 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).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".