Internal instability mechanism of glacier deposits: insights from seepage deformation experiment
Bibliographic record
Abstract
Glacial deposits, formed through glacial erosion, transportation, and accumulation, are exposed in significant quantities because of global warming, serving as potential sources of hazardous geological events. This study undertook a comprehensive analysis of glacial deposits in the upper reaches of the Yi'Ong Zangbo River in Tibet, China. Through field investigations, original sample seepage deformation failure experiments, and laboratory experiments, the formation mechanisms and permeability properties of glacial deposits in this basin were explored, with particular emphasis on the effect of fine particle loss on their permeability characteristics. The findings revealed that the glacial deposits in the Xibengnongba basin of the Yi'Ong Zangbo river were formed during the Quaternary glacial period and exhibited a unique initial structure influenced by glacial transport. Based on the Q –i curve derived from the seepage deformation experiment, the glacial deposits seepage process was categorized into the stable seepage stage, internal suffusion stage, and failure stage, with evident piping and localized fine particle migration observed during the internal suffusion stage. Furthermore, due to the disturbance of the original structure, the reconstructed glacier deposit demonstrated lower critical hydraulic gradients and higher levels of fine particle loss.
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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 | 0.000 |
| 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) | 0.001 | 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".