A study on the deep structure of the northern part of southwest sub-basin from ocean bottom seismic data,South China Sea
Bibliographic record
Abstract
Based on the OBS data and the multi-channel seismic data collected in 2009 and 2011, the structure of crust in the north part of Southwest Sub-basin in the South China Sea is explored.Using two-dimensional ray tracing method to establish P velocity model,employing acoustic basement reflection,the reflections from the top interface of lower crust and the Moho are calculated in order to give the location of the velocity discontinuity 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 lower crust and the Moho vary significantly.We inferred the southwest sub-basin rifting model,and concluded that there is a similarity in geological structure between the southwest sub basin of South China Sea and Iberia Newfoundland which is a typical non-volcanic margin.It is found that the two end-member mechanical continental rifting models could not satisfactorily explain the southwest sub-basin geological structure,while the elastic beam mechanics model can explain this rifting mode.Although in the lower crust of the northern part of southwest sub-basin there isn't large-scale magmatie activity,we inferred that a small amount of molten material may exist on the top of 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 | 0.002 |
| 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".