Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: implications for remote sensing validation networks
Bibliographic record
Abstract
Abstract Several large in-situ soil moisture-monitoring networks currently exist over seasonally frozen regions that may have use for the validation of remote sensing soil freeze/thaw (F/T) products. However, further understanding of how the existing network instrumentation responds to changes in near surface soil F/T is recommended. This case study describes the results of a small plot-scale (7 × 7 m) study from November 2013 through April 2014 instrumented with 36 impedance probes. Soil temperature and real dielectric permittivity ϵr' were measured every 15 minutes during F/T transition periods at shallow soil depths (0–10 cm). Categorical soil temperature and real dielectric permittivity techniques were used to define the soil F/T state during these periods. Results demonstrate that both methods for detecting soil F/T have strong agreement (84.7–95.6%) during the fall freeze but weak agreement (53.3–60.9%) during the spring thaw. Bootstrapping results demonstrated both techniques showed a mean difference within ±1.0°C and ±1.4 ϵr' between the standard 5 cm below surface measurement depth and probes at 2, 10 and integrated 0–5.7 cm depths installed within the same study plot. Overall this study demonstrates that the Hydra Probe offers promise for near surface soil F/T detection using existing soil moisture monitoring networks particularly for the fall freeze.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".