Closure in concentration data: molar element ratio analysis of compositional variations in Castlepoint, New Zealand mudstones
Bibliographic record
Abstract
Quantitative interpretations of lithogeochemical datasets using Pearce element ratio (PER) analysis have revealed new information far beyond that acquirable using conventional element concentration scatterplots and statistics. Historically, most of these PER data interpretations have revealed new information about fresh and hydrothermally altered igneous rocks. As such, they have quantitatively provided new insights into lithogenetic and metasomatic processes. In contrast, molar element ratio (MER) analysis of sedimentary rocks has not been routinely undertaken, but its proper application can reveal new information about the deposition of sedimentary rocks, as well as their subsequent diagenesis. This paper investigates the compositional variability of 24 samples of mudstones from a well-described and sampled turbidite sequence in the Miocene Whakataki Formation, on the east coast of the North Island of New Zealand. Background data include whole rock and trace element concentrations from these T D and T E Bouma facies (parallel laminated and homogeneous mud), along with detailed X-ray diffraction, and mineralogical and size fraction information. These data provide a substantial petrographic foundation for data interpretation using MER analysis. Results reveal that several material transfer processes served to modify the compositions of these mudstones over time. Gravitational sorting of quartz, or quartz overgrowths (but not feldspar sorting), occurred during sediment deposition and/or diagenesis. Precipitation of impure siderite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>±</mml:mo> </mml:math> apatite concretions and calcite cement, and alteration of feldspar to illite occurred during diagenesis. Appropriately constructed PERs not only provide quantitative estimates of the extent of these processes, but illustrate that the method can facilitate, detect and detail an understanding of many subtle to obvious material transfer processes in clastic sedimentary rocks. Thus, PER analysis represents an effective method with which geoscientists can understand the causes of compositional variability in the sedimentary realm using lithogeochemical data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".