Imaging periglacial conditions with ground‐penetrating radar
Bibliographic record
Abstract
Abstract Three important parameters that need to be quantified for many permafrost studies are the location of ice in the ground, the position of thermal interfaces, and spatial variations of the water content in the active layer. The data from over 100 investigations in permafrost regions demonstrate that ground‐penetrating radar (GPR) offers an effective way to measure these parameters at a scale appropriate for many process and geotechnical studies. Horizontal to gently‐dipping interfaces between unfrozen and frozen subsurface zones (such as at the base of the active layer or a suprapermafrost talik) were repeatedly detected by GPR and indicated by strong, laterally‐coherent reflections. Coherent reflections are not generated by steeply dipping thermal interfaces (greater than 45°). However, the transition from frozen to unfrozen ground can frequently be located from the radar‐stratigraphic signatures of the two units. The radar‐stratigraphic signature of excess ice in the subsurface is determined by the size of the body. Ice lenses that are smaller than the resolution of the GPR system frequently can be detected and are represented by chaotic or hyperbolic reflections, while the size of larger ice units can be resolved and is defined by distinct laterally‐coherent reflection patterns. This enables the delineation of the vertical and lateral extent of massive ice bodies, and their structural setting. By making precise measurements of the direct ground wave velocity, the water content in the near‐surface can be determined for uniform soils. It is demonstrated that by collecting a grid of GPR data the lateral variations in active‐layer water content can then be estimated. Copyright © 2003 John Wiley & Sons, Ltd.
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.
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) | 0.001 | 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".