Sequence stratigraphy: common ground after three decades of development
Bibliographic record
Abstract
Sequence stratigraphy emphasizes changes in stratal stacking patterns in response to varying accommodation and sediment supply through time. Certain surfaces are designated as sequence or systems tract boundaries to facilitate the construction of realistic and meaningful palaeogeographic interpretations, which, in turn, allows for the prediction of facies and lithologies away from control points. Precisely which surfaces are selected as sequence boundaries varies from one sequence stratigraphic approach to another. In practice, the selection is often a function of which surfaces are best expressed, and mapped, within the context of each case study. This high degree of variability in the expression of sequence stratigraphic units and bounding surfaces requires the adoption of a methodology that is sufficiently flexible to accommodate the wide range of possible scenarios in the rock record. We advocate a model-independent methodology that requires the identification of all sequence stratigraphic units and bounding surfaces, which can be delineated on the basis of facies relationships and stratal stacking patterns using the available data. Construction of this framework ensures the success of the method in terms of its objectives to provide a process-based understanding of the stratigraphic architecture and predict the distribution of reservoir, source-rock, and seal facies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".