Magmatic affinity of modern and ancient subalkaline volcanic rocks determined from trace-element discriminant diagrams
Bibliographic record
Abstract
When dealing with ancient subalkaline volcanic rocks, the alkali – total iron – magnesium (AFM) diagram is of limited use in assigning a tholeiitic versus calc-alkaline affinity because these elements are often mobile during alteration and metamorphism. Classification diagrams using immobile trace elements are preferable but need to be tested and optimized on unaltered rocks. To this end, a geochemical database containing over a thousand, presumed unaltered, subalkaline volcanic samples from young oceanic arcs was assembled. The data were classified using both major and trace-element approaches, and the results were compared. If the calc-alkaline and tholeiitic fields on the AFM diagram are used to define magmatic affinity, then the commonly used Zr versus Y, La versus Yb, and Th versus Yb discriminant diagrams misclassify 39%, 24%, and 28% of samples, respectively. After optimization (using a number of criteria), all three trace-element classification diagrams produce results that are generally consistent with the AFM diagram. The optimized diagrams only misclassify 7%, 11%, and 12% of the samples, respectively. A new Th/Yb versus Zr/Y diagram has a better overall performance than any single ratio diagram and may prove helpful in assigning magmatic affinities to volcanic rocks in ancient successions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".