Imaging groundwater beneath a rugged proglacial moraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it