Glacier and landslide feedbacks to topographic relief in the Himalayan syntaxes
Bibliographic record
Abstract
Despite longstanding research on the age and formation of the Tibetan Plateau, the controls on the erosional decay of its margins remain controversial. Pronounced aridity and highly localized rock uplift have traditionally been viewed as limits to the dissection of the plateau by bedrock rivers. Recently, however, glacier dynamics and landsliding have been argued to retard headward fluvial erosion into the plateau interior by forming dams and protective alluvial fill. Here, we report a conspicuous clustering of hundreds of natural dams along the Indus and the Tsangpo Rivers where these cross the Himalayan syntaxes. The Indus is riddled by hundreds of dams composed of debris from catastrophic rock avalanches, forming the largest concentration of giant landslide dams known worldwide, whereas the Tsangpo seems devoid of comparable landslide dams. In contrast, glacial dams such as river-blocking moraines in the headwaters of both rivers are limited to where isolated mountain ranges intersect the regional snowline. We find that to first-order, high local topographic relief along both rivers corresponds to conspicuously different knickzones and differences in the type and potential longevity of these dams. In both syntaxes, glacier and landslide dams act as a negative feedback in response to fluvial dissection of the plateau margins. Natural damming protects bedrock from river incision and delays headward knickpoint migration, thereby helping stabilize the southwestern and southeastern margins of the Tibetan Plateau in concert with the effects of upstream aridity and localized rock uplift.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".