The reconstruction of coastal carbonate sequence stratigraphy: A modern-systems approach
Bibliographic record
Abstract
Abstract Sequence stratigraphy is the primary tool used by sedimentologists to predict bed-scale flow properties of both marine carbonate reservoirs (associated with carbon sequestration and hydrocarbon recovery) and groundwater aquifer systems. Coastal carbonate sequence stratigraphic models have been predicated upon the existence of parasequences, shallowing-upward successions bounded by marine flooding surfaces. Transgressive deposits in such models have been assumed to be mostly absent, whereas regressive deposits are presumed to form through the successive basinward stepping of shoals and their associated lagoons over open platforms. A review of modern coastal systems calls these assumptions into question. Transgressive deposits are substantive. In particular, in situ lagoonal and tidal-flat deposits left behind by overriding landward-migrating barriers are common across Holocene carbonate platforms. Furthermore, regressive deposits are not represented by prograding shoals and lagoons, but by lagoonal abandonment, and grainy shoreface progradation capped by strandplains, with overlying accommodation restricted to swales where only thin, discontinuous, intertidal mud flats form. We present a novel sequence-stratigraphic model based on these modern-systems observations with significant implications for subsurface geobody connectivity and fluid-flow prediction.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".