A repository of measured soil freezing characteristic curves: 1921 to 2021
Bibliographic record
Abstract
Abstract. Soil freeze–thaw processes play a fundamental role in the hydrology, geomorphology, ecology, thermodynamics, and soil chemistry of cold regions' landscapes. In understanding these processes, the temperature of the soil is used as a proxy to represent the partitioning of soil ice and water content via 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 and composition), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and the degree of saturation, all of which can be site-specific and time-varying characteristics. SFCCs have been measured using various methods for diverse soils since 1921, but, to date, these data have not been broadly compared. This is in part because they had not previously been compiled in a single dataset. The dataset presented in this publication includes SFCC data digitized or received from authors, and it includes both historic and modern studies. The data are stored in an open-source repository, and an R package is available to facilitate their use. Aggregating the data has pointed out some data gaps, namely that there are few studies on coarse soils and comparably few in situ measurements of SFCCs in mountainous environments. It is hoped that this dataset (https://doi.org/10.5281/zenodo.5592825; Devoie et al., 2022a) will aid in the development of SFCC theory and improve SFCC approximations in soil freeze–thaw modelling activities.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.029 |
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".