Cryowurst: a wireless borehole instrument for observing hydrology and ice kinematics in surging glaciers
Bibliographic record
Abstract
Glacier surges are dramatic increases in glacial ice flow velocity occurring over short periods of time (months to years), which can lead to rapid advance of the ice front and trigger hazardous outburst flooding in local areas. Direct measurements of the basal hydrology and internal dynamics of surging glaciers are sparse, due to the limitations of wired instrumentation and the unpredictability of surge timing. Consequently, the causes of surge events are poorly understood, and we are unable to accurately predict their occurrence.We have developed a borehole instrument, the sausage-shaped "Cryowurst", which can wirelessly transmit measurements of temperature, electrical conductivity, pressure and tilt within and beneath a glacier to the surface over a period of multiple years. These sensors allow us to directly measure the hydrological conditions and kinematics of ice deformation, over longer time periods than is currently possible with wired instrumentation due to cable breakage.We installed a vertical string of four Cryowursts 20-50m apart in a hot-water-drilled borehole in Dän Zhùr (Donjek Glacier), a surging glacier in the Yukon territory of Canada, which is predicted to surge before 2027. We present preliminary data on the basal hydrology and internal kinematics of the glacier, which were transmitted through up to 170m of ice, and received at a solar-powered and satellite-enabled receiving station on the glacier surface. Based on recent testing, there is potential for these instruments to transmit data continuously over multiple years, capturing novel information about the causes and consequences of glacier surging.
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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 | 0.000 | 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".