Eggs and sausages: wireless instrumentation for measuring ice anisotropy and kinematics
Bibliographic record
Abstract
Field observations of ice flow properties on large temporal and spatial scales are vital to improve our understanding of ice sheet and glacier dynamics. However, we are currently limited in what we can observe, and on what timescales, with wired instrumentation and remote sensing. We present preliminary tests of wireless instrumentation to measure the kinematics and anisotropy of flowing ice.We used a spherical probe ("cryoegg") emitting VHF radio waves to measure birefringence in 19 azimuthal directions around a borehole in the Northeast Greenland Ice Stream (NEGIS). From these data we are able to infer information about crystal anisotropy in the ice in three dimensions, and compare with a transfer matrix radio propagation model. This is a significant improvement on previous monostatic radar methods, which are limited to observations of crystal orientations in the horizontal plane.Additionally, we present initial observations of borehole tilt, temperature, pressure and conductivity from Donjek Glacier, Canada, collected using wireless borehole instruments ("cryowursts''). These data were transmitted through up to 170m of ice, and received at a solar-powered and satellite-enabled receiving station on the glacier surface. There is potential for these instruments to transmit data continuously from surging glaciers over multiple years.These preliminary studies demonstrate new possibilities for collecting exciting long-term datasets for glaciology.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".