A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves
Bibliographic record
Abstract
Abstract. Soil freeze-thaw processes play a fundamental role in the hydrology, geomorphology, ecology, thermodynamics and soil chemistry of a cold-regions landscape. In understanding these processes, the temperature of the soil is used as a proxy to represent the soil ice content through a soil freezing characteristic curve (SFCC). This mathematical construct relates the soil ice content to a specific temperature for a particular soil. SFCCs depend on many factors including soil properties (e.g., porosity, composition, etc.), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and degree of saturation, all of which can be site-specific and time varying. SFCCs have been measured using various methods for diverse soils since 1921, and to date this data has not been broadly compared, in part because it has not previously been compiled in a single data set. The dataset presented in this publication includes SFCC data digitized or received from authors, and includes both historic and modern studies. The data is stored in an open-source repository, and an R package is available to facilitate its use. Aggregating the data has pointed out some data gaps, namely that there are few studies of coarse soils, and comparably few in-situ measurements of SFCCs in mountainous environments. It is hoped that this dataset will aid in the development of SFCC theory, and improve SFCC approximations in soil freeze-thaw modelling activities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.030 |
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".