C20-C21-C23 tricyclic terpanes abundance patterns: Origin and application to depositional environment identification
Bibliographic record
Abstract
Reconstruction of paleo-depositional environments in a sedimentary basin is often obstructed by the absence of typical environmental indicators in sedimentary rocks. Here, we propose a biomarker method using C 20 -C 21 -C 23 tricyclic terpanes (TTs) as a tracer, which is simple in analysis but robust to provide reliable and detailed environmental information. Based on the analysis of 271 C 20 -C 21 -C 23 TT data from 32 basins in 18 countries, we observed a relationship between C 20 -C 21 -C 23 TT abundance patterns and depositional environments. This relationship was attributed to the control of depositional environments on the input proportions of plankton and terrigenous plants, which act as two end-member precursors for the TTs in a depositional system. The various mixing proportions between these two end-members result in different C 20 -C 21 -C 23 TT abundance patterns associated with different depositional environments, e.g., C 20 >C 21 >C 23 TT in river-lake transitional, C 20 <C 21 <C 23 TT in marine or saline lacustrine environments, C 20 <C 21 >C 23 TT in freshwater lacustrine and C 20 >C 21 <C 23 TT in marine-continental transitional environments. In addition, the C 23 /C 21 TT ratio increases with elevated salinity of depositional water, and the C 21 /C 20 TT ratio increases with increasing water depths. Based on these observations, a discrimination diagram using C 23 /C 21 TT vs. C 21 /C 20 TT was developed for environmental identification. The validity of this C 20 -C 21 -C 23 TT biomarker method is well demonstrated by the rock samples with typical environmental indicators. This method is applicable in a broad spectrum of rocks and in maturities up to 2.4%Ro. Its strength was shown by a case study of a complex depositional system in the East China Sea Basin, which has been strongly affected by eustasy.
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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 | 0.001 | 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) | 0.000 | 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".