The extent of igneous rocks of the South China Sea based on the correlational analyses of gravity and magnetic data
Bibliographic record
Abstract
<p>The South China Sea is the biggest conjugate marginal sea in the West Pacific Ocean, which is influenced by the Eurasian plate, the Pacific plate, and the Indo-Australian plate. There have developed continental tectonic margins with different characters after experiencing subduction, collision, strike-slip and so on since the Mesozoic and Cenozoic (Yao et al., 2004; Zhang et al., 2014). However, the igneous rock can be regarded as a recorder to reveal some information of evolution and deep geodynamics of the South China Sea, which helps us to improve understanding of the continental rifting, the seafloor spreading, the formation of deep water basins and the process of hydrocarbon accumulation(Zhang et al., 2016).<br>The igneous rocks are studied by multiple types of data that are magnetic data, seismic profiles, and drilling data in the previous studies. Hence, there are bunch of research results about the igneous rocks that contain the reason and time of formation, the distribution of space, the period of eruption in the north of the South China Sea because of the abundant datasets (Zou et al., 1993,1995; Zhou et al., Yan and Liu, 2005; Xu et al., 2013; Zhang et al., 2013; Zhang et al., 2014; Zhang et al., 2015; Zhang et al., 2016), in addition, the Pearl River Mouth Basin is the most famous one among all of the basins in the South China Sea. However, the researchs related to the south of the South China Sea where are the deep-sea are far less knowledgeable about the distribution of the igneous rocks than the north because of the limitation of datasets that are poor quality and less quantity (Yao et al., 2004; Li et al., 2010; Hui et al., 2016), which lead to the less researches with respect to the big area of the South China Sea.<br>The followings can be concluded from the previous studies. The northern and continental margin of the South China Sea are distributed by Cenozoic extrusive rocks with high susceptibility and low density and Yanshanian intrusive rocks with low susceptibility and density (Hao et al., 2009; Lu et al., 2011; Hui et al., 2016), the Central Sub-basin is covered by Cenozoic extrusive rocks (Yan and Liu, 2005; Hui et al., 2016), however, there are few distributions of the Yanshanian intrusive rocks in the Southern South China Sea (Zhang et al., 2015; Hui et al., 2016). In this study, a new method, the fusion of gravity and magnetic data, is applied to detect the distribution of the igneous rocks in order to provide more geophysical data in the South China Sea.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".