Internal Structure and Composition of a Rock Glacier in the Dry Andes, Inferred from Ground‐penetrating Radar Data and its Artefacts
Bibliographic record
Abstract
Abstract Using ground‐penetrating radar (GPR), we studied an entire 2.2 km long rock glacier (3780–4350 m asl) in the dry Andes of Chile with the aim of inferring its composition. In the high‐quality, unmigrated data, we identified the active layer base and the rock glacier floor. In between, hyperbolae generated by diffracting boulders were inventoried; the ones along the rock glacier floor (n = 51) allowed determination of the average electromagnetic (EM) velocity in the rock glacier, the latter being further used for migration. Within the rock glacier (16–39 m thick), the EM velocity varies between 0.076 and 0.167 m.ns‐1; the main stratigraphic features observed are upward‐dipping reflectors. The low EM velocities (<0.10 m.ns‐1) found at some locations suggest the presence of significant unfrozen water fractions. A strong (R2 = 0.77), inverse linear relationship was also found between the diffracting point density and the EM velocity, and was used to indicate the ice content in the rock glacier. The fraction of ice in the rock glacier was estimated to vary between 0.22 and 0.83, with an average of 0.66; these results were tested by recalculation of the EM velocity. The relationships between rock glacier development and glacial processes are questioned. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".