Quantitative bedrock geology of east and Southeast Asia (Brunei, Cambodia, eastern and southeastern China, East Timor, Indonesia, Japan, Laos, Malaysia, Myanmar, North Korea, Papua New Guinea, Philippines, far‐eastern Russia, Singapore, South Korea, Taiwan, Thailand, Vietnam)
Bibliographic record
Abstract
We quantitatively analyze the area‐age distribution of sedimentary, igneous and metamorphic bedrock based on data from the most recent digital geologic maps of East and Southeast Asia ( Coordinating Committee for Coastal and Offshore Geosciences Programmes in East and Southeast Asia (CCOP) and the Geologic Survey of Japan , 1997; 1:2,000,000), published as Digital Geoscience Map G‐2 by the Geological Survey of Japan. Sedimentary rocks, volcanic rocks, plutonic rocks, ultramafic rocks and metamorphic rocks cover 73.3%, 8.5%, 8.8%, 0.9%, and 8.6% of the surface area, respectively. The average ages of major lithologic units, weighted according to bedrock area, are as follows: sedimentary rocks (average stratigraphic age of 123 Myr/median age of 26 Myr), volcanic rocks (84 Myr/20 Myr), intrusive rocks (278 Myr/195 Myr), ultramafic rocks (unknown) and metamorphic rocks (1465 Myr/1118 Myr). The variability in lithologic composition and age structure of individual countries reflects the complex tectonic makeup of this region that ranges from Precambrian cratons (e.g., northeast China and North Korea) to Mesozoic‐Cenozoic active margins (e.g., Japan, the Philippines, Indonesia and New Guinea). The spatial resolution of the data varies from 44 km 2 per polygon (Japan) to 1659 km 2 per polygon (Taiwan) and is, on average (490 km 2 /polygon), similar to our previous analyses of the United States of America and Canada. The temporal and spatial resolution is sufficiently high to perform age‐area analyses of individual river basins larger than ∼10,000 km 2 and to quantitatively evaluate the relationship between bedrock geology and river chemistry. As many rivers draining tropical, mountainous islands of East and Southeast Asia have a disproportionate effect on the dissolved and particulate load delivered to the world oceans, bedrock geology in such river drainage basins disproportionately affect ocean chemistry.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".