Bibliographic record
Abstract
Abstract. Climate change is accelerating cryosphere degradation in mountainous regions, altering hydrological and geomorphological dynamics in deglaciating catchments. Among cryospheric features, rock glaciers degrade more slowly than glaciers, providing a sustained influence on water resources in alpine watersheds. This study investigates the role of a rock glacier interacting with the Shár Shaw Tagà River (Grizzly Creek) riverbed in the St. Elias Mountains (Yukon, Canada), using a unique multimethod approach that integrates hydro-physicochemical and isotopic characterization, drone-based thermal infrared (TIR) imagery, and visible time-lapse (TL) imagery. Results assess that rock glaciers, due to their geomorphic properties, can constrict riverbeds and alluvial aquifers, and control shallow groundwater flow, leading to notable changes in channel structure and groundwater discharge. These disruptions promote downstream cryo-hydrological processes by facilitating aufeis formation and modifying the physicochemical properties of streamflow. Additional findings highlight the critical role of rock glaciers and proglacial systems in connecting mountain cryosphere and deep groundwater systems, with consequent implications for mountain hydrology and water resources.
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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.189 | 0.122 |
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".