Imaging groundwater beneath a rugged proglacial moraine
Bibliographic record
Abstract
ABSTRACT With the changing precipitation patterns and melting of mountain glaciers and permafrost that result from global warming, information on the distribution of groundwater in mountainous terrains is becoming increasingly important for developing prudent resource and hazard management strategies. Obtaining this information across topographically craggy and variably frozen ground in a cost-effective and nonintrusive manner is challenging. We introduce a modified 2D surface nuclear magnetic resonance (NMR) tomographic technique that allows us to account for substantial variations in surface topography in locating and quantifying groundwater occurrences in rugged mountains. Because contact with the ground is not necessary, it is a rare geophysical technique not affected by sensor-to-ground coupling problems common in high mountain environments. To demonstrate the efficacy of the tomographic imaging scheme, we invert a large multioffset surface NMR data set collected across a partially ice-cored proglacial terminal moraine in the Canadian Rocky Mountains. Our preferred model contains a 2- to 5-m-thick water layer, the top of which has practically the same elevation as the surface of a nearby lake and the bottom of which coincides with bedrock resolved in companion seismic and ground-penetrating radar studies.
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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) | 0.000 | 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".