Deep Crustal Structure of the Northern Part of Southwest Sub‐Basin, South China Sea, From Ocean Bottom Seismic Data
Bibliographic record
Abstract
Abstract Based on the OBS data and the multi‐channel seismic data collected in 2009 and 2011, the structure of the crust in the north part of the southwest sub‐basin in the South China Sea is explored. The two‐dimensional ray tracing method is used to establish P velocity model; the reflections from the acoustic basement, the top of lower crust, and the Moho are employed to give the location of the discontinuous velocity interfaces. By employing refraction under the acoustic basement and the head wave from the mantle, the P wave velocity structure of the entire survey line is pictured. The results showed that the depths of the top interface of the lower crust and Moho are varying. We inferred the southwest sub‐basin rifting model, and there is certain similarity in geological structure between the southwest sub‐basin of the South China Sea and Iberia‐Newfoundland which is a typical non‐volcanic margin. The conclusion is that the two end‐member continental rifting models could not explain satisfactorily the geological structure of southwest sub‐basin, while the elastic beam model can explain this extension mode. Although large‐scale magmatic activity was not found in the lower crust of the northern part of southwest sub‐basin, we inferred that a small amount of molten material may exist on the top of the Moho.
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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) | 0.001 | 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".