Identifying marine transgressive-regressive depositional cycles in distal shelf mudstones from the Western Interior Seaway of North America using high-resolution (500 um) chemostratigraphy of the Upper Cretaceous Mancos Shale-Datasets
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Bibliographic record
Abstract
High-resolution elemental data collected from an Itrax micro-X-ray fluorescence core scanner can be used to develop chemostratigraphic profiles of visually homogeneous mudstones that can potentially be correlated with near-shore facies with much higher accuracy than with lithology and well-log data alone. Our data, which was collected at 0.5-mm sampling resolution from 92 metres of the Upper Cretaceous Mancos Shale from North America, showed transgressive-regressive marine cycles based on siliciclastic input, terrestrial vs marine-dominant sedimentation, and relative redox and organic matter conditions in the sediment at sub-Milankovitch (i.e., millennial scale) frequency; this has not previously been documented in the literature over this length of core. By clustering the data using a hierarchical clustering algorithm known as a Self-Organising Map, we were able to also create detailed chemofacies for every sampling interval to compare the overall elemental signatures in the sediments and describe the depositional environment (prodelta, mudbelt, shelf) and relative proximity to shoreline. The elemental data are presented in total counts of fluoresced X-rays for each element. As the total number of fluoresced X-rays will be dependent on the energy of the incident X-rays as well as the exposure time, both of which are user-defined parameters, the data are semi-quantitative and do not provide information on the absolute amount (e.g., wt.%, ppm, ppb) of any given element. Each folder contains the individual elemental data files, based on core depth in feet below the surface, as tab-separated .txt files (e.g., C15_6557-6561.txt) as well as the accompanying core image (e.g., C15_6557-6561.tif) that is collected by the Itrax at the same time as the elemental data allow for direct comparison. Additionally, the full dataframe containing all intervals (MancosCore_final.csv), the row-wise centred dataframe used in the Self-Organising Map (MancosZ_final.csv), and the clusters (MancosClusters_final.csv) are also included as comma-separated values. For further explanation of the clustering, the reader is referred to the manuscript of the same title in Palaeogeography, Palaeoclimatology, Palaeoecology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it