Sedimentary characteristics and processes of the Paleogene Dainan Formation in the Gaoyou Depression, North Jiangsu Basin, eastern China
Bibliographic record
Abstract
In this paper, the type, vertical evolution, and distribution pattern of sedimentary facies of the Paleogene Dainan Formation in the Gaoyou Depression of the North Jiangsu Basin are studied in detail. Results show that fan delta, delta, nearshore subaqueous fan, and lacustrine facies developed during the Dainan Formation period and their distribution pattern was mainly controlled by tectonics and paleogeography. The fan delta and nearshore subaqueous fan facies predominantly occur in the southern steep slope region where fault-induced subsidence is thought to have created substantial accommodation, whereas the delta facies are distributed on the northern gentle slope which is thought to have experienced less subsidence. Finally, the lacustrine facies is shown to have developed in the center of the depression, as well as on the flanks of the fan delta, delta, and nearshore subaqueous fan facies. Vertically, the Dainan Formation represents an integrated transgressive–regressive cycle, with the E2 d 1 being the transgressive sequence and the E2 d 2 being the regressive sequence. This distribution model of sedimentary facies plays an important role in predicting favorable reservoir belts for the Dainan Formation in the Gaoyou Depression and similar areas. In the Gaoyou Depression, sandstones of the subaqueous distributary channels in the fan delta and the subaqueous branch channels in the delta are characterized by physical properties favorable for reservoir formation.
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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 | 0.001 | 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 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".