Field observations of shallow freeze and thaw processes using high‐frequency ground‐penetrating radar
Bibliographic record
Abstract
Abstract We have used reflection profiles and common‐midpoint (CMP) soundings with 900 MHz ground‐penetrating radar (GPR) to monitor freezing and thawing processes during winter seasonal periods at two separate sites located in Ontario, Canada. GPR responds to the large contrast in dielectric permittivity between liquid water and ice. The profiles reveal the long‐term development of a very shallow (<0·5 m) soil frost zone overlying unfrozen wet substratum. During the course of the winter season, long‐term travel time analysis yielded physical properties of the frozen and unfrozen layers as well as the spatial distribution of the base of the soil frost zone. Short‐term shallow thawing events overlying frozen substratum formed a dispersive waveguide for both the CMP and reflection profile surveys. Inversion of the dispersive wavefields for the CMP data yielded physical property estimates for the thawed and frozen soils and thawed layer thickness. We have shown that GPR can be used to monitor very shallow freezing and thawing events by responding to changes in the relative dielectric permittivity of the soil water phase (e.g. liquid water vs ice). The non‐invasive collection of such data permits interpretation of dynamic temporal and spatial freeze–thaw events, which are important for characterizing a range of hydrological processes. Copyright © 2010 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 | 0.000 |
| 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".