A three‐dimensional Moho depth model for the Tien Shan from EGM2008 gravity data
Bibliographic record
Abstract
The Tien Shan in Central Asia is the largest intracontinental mountain range in the world, but it is 1500 km away from the collision zone between the Indian and Eurasian plates. This region has been and still is the focus of numerous geoscientific studies, mainly because of its evolutionary history and its unique position in the Eurasian lithosphere plate. So far, mainly seismological data have been used to explore the origin of and ongoing seismic activity in this region, but only one study has investigated terrestrial gravity data. In this study, a new gravity data set, EGM2008, is used to determine the crust‐mantle boundary (Mohorovičić discontinuity, Moho) of the Tien Shan using inversion of gravity data. In addition, an isostatic Moho is calculated from topographic data, which by comparison to the results of the gravity inversion illuminates the effects of isostatic compensation. The results of the gravity inversion generally agree with results of previous seismic studies and indicate that the Tien Shan has a mountain root with a thickness of about 75 km. Furthermore, the Moho is shallow under the basins, e.g., in the Tarim and Ili basins. The comparison with the isostatic Moho indicates an over‐compensation of the orogen and an under‐compensation of the basins. The over‐compensation results from the former subduction of the Tarim Basin terrane in the south. The under‐compensation of the Tarim Basin is generated by support of the terrane between the Tien Shan in the north and the Pamir mountains, Tibet and Himalayas in the south.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".