A new framework for estimating englacial water content and pore geometry using combined radar and seismic wave velocities
Bibliographic record
Abstract
Ice mechanical properties, and hence the response of glaciers to climate change, depend strongly on the presence of liquid water at ice‐grain boundaries. The propagation velocities of radar and seismic waves are also highly sensitive to this water. Mixing laws, typically the Looyenga and Riznichenko formulae, have traditionally been used to quantify liquid water content within glaciers from such velocity data; however, it has become apparent that these mixing laws are geometrically inconsistent. We present an inclusion‐based effective medium approximation in which we model water inclusions within solid ice. Two types of inclusions are used: spherical inclusions to represent water in the grain junction nodes, and high‐aspect ratio spheroidal inclusions to represent water in the grain boundary veins. We apply this model to radar and seismic data from a polythermal glacier in Svalbard to quantify both inclusion geometry and the unfrozen water content within the warm ice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".