Establishing detailed chemofacies of depositional environments in an epeiric seaway using high‐resolution (500 μm) m<scp>icro X‐ray fluorescence</scp> core scanning data
Bibliographic record
Abstract
ABSTRACT Establishing depositional environments in ancient mudstone successions from epeiric seas is difficult due to the lack of obvious lithological changes, leaving long, largely undifferentiated mudstone intervals that complicate their correlation to near‐shore environments. This problem is mainly the result of the limitations in analytical resolution using traditional methods, making it difficult, if not impossible, to accurately identify transitions between depositional environments. This study used elemental data collected from an Itrax micro X‐ray fluorescence core scanner at 500‐μm sampling interval to establish detailed chemofacies in a thick (17 m) distal mudstone deposit and compare them to the chemofacies of previously established near‐shore (fluvial floodplain to prodelta) depositional environments. The chemofacies for the mudstone were created using a hierarchical clustering algorithm known as a self‐organizing map, to develop detailed descriptions of elemental composition, which showed the variation both between environments and within them. The relationship between Fe (terrigenous proxy) and Ca (marine proxy) was effective at indicating proximity to shoreline while the relationships between Ti and K describe weathering and transport conditions at the sediment–water interface due to changes in bottom‐water current energy. For the near‐shore sediments, the average values in the elemental proxies were less effective at distinguishing the environments than the SDs of those proxies within each environment, which became more constrained (lower relative to ) basinwards as fluvial input and water energy decrease. Basinwards of the prodelta, the values of terrigenous proxies decreased more rapidly and were associated with a rapid increase in mean Ca values and SDs, combined with an increase in V/Cr and Cu/Ti as a result of lower oxygen conditions and increased preservation of marine organic matter. These robust chemofacies can help to guide the lithological interpretation and allow for higher resolution mapping of ancient mudstone sequences from epeiric seas, which will improve their correlation to near‐shore environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".