A quantitative method for deriving salinity of subglacial water using ground-based transient electromagnetics
Bibliographic record
Abstract
Abstract Liquid water can exist at temperatures well below freezing beneath glaciers and ice sheets, where subglacial water systems, fresh and saline, have been shown to host unique microbial ecosystems. Geophysical techniques sensitive to fluid-content contrasts, e.g. electromagnetics, can characterize subglacial water and its salinity. Here, we assess the ground-based transient electromagnetic (TEM) method for deriving the resistivity and salinity of subglacial water. We adapt an existing open-source Bayesian inversion algorithm, which uses independent depth constraints, to output posterior distributions of resistivity and pore fluid salinity with depth. A variety of synthetic models, including a thin (5 m), conductive (0.16 Ωm), hypersaline (147 psu) subglacial lake, are used to evaluate the TEM method for imaging under 800 m-thick ice. The study demonstrates that TEM methods can resolve conductive, saline bodies accurately using external depth constraints, for example, from radar or seismic data. The depth resolution of TEM can be limited beneath deep (>800 m), thick (>50 m) conductive, water bodies and additional constraints from passive electromagnetic (EM) methods could be used to reduce ambiguities in the TEM results. Subsequently, non-invasive active and passive EM methods could provide profound insights into remote aqueous systems under glaciers and ice sheets.
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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 | 0.000 |
| 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) | 0.001 | 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".