Mapping the thickness of slush on sea ice with multi-frequency EM induction sounding
Bibliographic record
Abstract
Slush from flooding of sea ice contributes significantly to the sea ice mass balance in the Arctic and Antarctic and poses significant hazards for Arctic communities, affecting the safe use of sea ice for travel, hunting, and other activities. This study demonstrates the effectiveness of multi-frequency electromagnetic (EM) induction sounding for the joint retrieval of slush and ice thicknesses. For the multi-frequency GEM-2 instrument, we identified optimal frequency combinations, for example 5, 10, 20, 30, and 93 kHz, through inversion of synthetic data with realistic noise to achieve minimal mean absolute errors (MAE) of less than 5 cm for slush as thick as 60 cm. Field EM surveys, validated with coincident drill hole data, demonstrated reliable performance of the method under practical field conditions for slush layers up to 20 cm thick. Instrument calibration was robust but faced challenges at sites where snow and ice conditions deviated from the ideal one-layer model for snow and ice. The inclusion of varying sea ice conductivities in the calibration process enhanced reliability, and we show that a single instrument calibration remains stable for over a week for this instrument of the newest GEM-2 generation. The method’s transferability to airborne applications, such as drone-mounted surveys, offers the potential to eliminate operator risks associated with ground-based measurements on thin ice with thick slush. Overall, multi-frequency EM induction sounding provides a time-efficient and accurate tool for mapping the separate thicknesses of slush and of snow-plus-ice thicknesses.
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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) | 0.000 | 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".