Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: Egbert, Ontario [Canada] raw dataset
Bibliographic record
Abstract
Several large in-situ soil moisture-monitoring networks currently exist over seasonally frozen regions which could have potential use for the validation of remote sensing soil freeze/thaw (F/T) products. However, due to our limited understanding of how the existing network instrumentation responds to changes in near surface soil F/T, these networks are largely ignored during the winter months. This case study describes the results of a small plot-scale (7 x7 m) study from November 2013 through April 2014 instrumented with 36 Hydra Probes. During the study, soil temperature and real dielectric permittivity were measured every 15 minutes during two F/T transition periods at shallow soil depths (0-10 cm). Categorical soil temperature and real dielectric permittivity techniques were then used to define the soil F/T state during these periods. The study showed that both methods for detecting soil freezing showed agreement (53.3- 60.9 %) during the spring thaw. Bootstrapping results demonstrated that both moderate to strong agreement with each other (84.7- 95.6 %) during the fall freeze but only weak techniques showed a mean difference within ±1.0°C and ±1.4 between the standard 5 cm below ground horizontal probe installation depth and the 2, 10 and integrated 0 -5.7 cm probe 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. The research also suggests that the device may have important applications for the validation of remote sensing F/T products.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".