Analysis of chemical weathering trends across three compositional dimensions: applications to modern and ancient mafic-rock weathering profiles
Bibliographic record
Abstract
Chemical weathering indices (one-dimensional/1D index values) and accompanying ternary plots (two-dimensional/2D compositional space) facilitate quantitative comparison of whole-rock and mineral major-element data, and empirical chemical trends with predicted weathering vectors. However, data analysis in ternary plots is restricted by poles grouping elements that are hosted in different minerals or that are influenced selectively by later alteration (e.g., diagenesis/metasomatism). Tetrahedral plots (three-dimensional/3D compositional space) offer enhanced analytical utility of major-element data by shifting elements across four poles and (or) incorporating additional proxy elements. Tetrahedral space can better reveal combined effects on major-element compositions from independent mineralogical controls and post-depositional alteration via curvilinear trends that are otherwise simplified and linear in ternary space. This study focuses on mafic-rock weathering and first reviews applications and limitations of the 1D mafic index of alteration (MIA) and index of lateritization/bauxitization (IOL/IOB) that integrate into molar Al 2 O 3 –CaO * –Na 2 O–K 2 O–(FeO (T) /Fe 2 O 3(T) )–MgO and SiO 2 –Al 2 O 3 –Fe 2 O 3(T) ternary compositional space, respectively. Analysis in tetrahedral space is then demonstrated with Phanerozoic weathering profile and Precambrian paleosol data in two plots of the molar Al 2 O 3 –CaO * –Na 2 O–K 2 O–(FeO (T) /Fe 2 O 3(T) )–MgO system (A–CN–K–FM and AF–CN–K–M plots) and one plot of the molar Al 2 O 3 –CaO * –Na 2 O–K 2 O–(FeO (T) /Fe 2 O 3(T) )–MgO–SiO 2 system (A–L–F–S plot). Common chemical weathering indices are integrated into these 3D tetrahedral spaces or onto some of their 2D ternary faces. However, the tetrahedral compositional space is a key to (1) assessing integrative effects from labile element loss while accounting for the variable, redox-dependent behaviour of Fe, (2) better exposing, and correcting for, overprinting effects of diagenesis/metasomatism, and (3) tracking Si loss across all stages of chemical weathering.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".