Petrogenesis of siliciclastic sediments and sedimentary rocks explored in three-dimensional Al<sub>2</sub>O<sub>3</sub>–CaO<sup>*</sup>+Na<sub>2</sub>O–K<sub>2</sub>O–FeO+MgO (A–CN–K–FM) compositional space
Bibliographic record
Abstract
Quantitatively determining the amount of chemical weathering within sedimentary rocks (and weathering profiles) took a major step forward with the creation of the chemical index of alteration (CIA) 40 years ago. The CIA relates the proportion of immobile aluminum to the mobile cations of calcium, sodium, and potassium and is grounded in empirical and modeled geochemical data for mineral reactions that occur during hydrolysis. However, the CIA should be applied cautiously because it is a one-dimensional value that in the most complex situations, as with clastic sedimentary rocks, homogenizes the compositional inputs of source, weathering, sorting, and diagenesis. Subsequently developed two-dimensional (2D) ternary diagrams (Al2O3–CaO*+Na2O–K2O; Al2O3–CaO*+Na2O+K2O–FeO+MgO) permitted the capacity to explore mineralogical-geochemical pathways in data sets that may separate those inputs, but interpreting the ternary diagrams may be complicated because they differentiate and group certain elements. Here we develop a three-dimensional tetrahedral diagram (Al2O3–CaO*+Na2O–K2O–FeO+MgO, A–CN–K–FM) that incorporates the same critical elements and permits the simultaneous assessment of felsic and mafic rocks and minerals on the same diagram while retaining the ability to separate plagioclase from alkali feldspar and monitor post-depositional potassium changes. Using the tetrahedral plot, we show that both the CIA value and positions on the 2D ternary diagrams can generate potentially misleading interpretations without properly budgeting the ferromagnesian components in parallel. We first show how the tetrahedron works, then use it with numerous previously published examples to identify how the competing mafic and felsic inputs shape the composition of source rocks, weathering profiles, actively transporting sediment, paleosols, and sedimentary rocks in sedimentary petrogenesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".