Timeline of the South Tibet – Himalayan belt: the geochronological record of subduction, collision, and underthrusting from zircon and monazite U–Pb ages
Bibliographic record
Abstract
The “exact timing” of collision between India and Eurasia is a recurring theme. Careful dating is critical for all tectonic events. With the example of the South Tibet – Himalaya collision system, a short review of arguments from different approaches suggests that this pursuit is in vain, but that our knowledge is already sufficient to provide an acceptably “precise” timing of the main events. We reviewed U–Pb ages of zircons and monazites, recognizing that major tectonic events can produce thermal effects strong enough to be recognized in high-temperature geochronology. This review also shows that precise timing is beyond the precision of the methods and the rock record. General consistency between geologic and thermochronologic records strengthens previous interpretations of the collisional orogenic system. We argue that the Tsangpo Suture in South Tibet results from two merged subduction zones and that island arcs may be part of the root of the Eurasian paleoactive margin, which is at variance with most tectonic interpretations. The two main collisional events closely followed each other at ca. 50 and 40 Ma.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".