A Scientometric Review of Research Status on Unfrozen Soil Water
Bibliographic record
Abstract
Unfrozen soil water affects the physical, chemical, hydrological, and mechanical properties of frozen soils, and climate change makes these relationships more complicated. The objective of this study was to investigate the research status of unfrozen soil water using scientometrics. Publications on unfrozen water in frozen soil (UWFS) retrieved from the Web of Science were analyzed with scientometric software tools including VOSviewer, CiteSpace, and HistCite Pro. The annual publication trend, co-authorship of authors, organizations, and countries, and the co-occurrence of keywords were analyzed. The most utilized journals and high-impact publications were identified. The results showed that 2007 (the year the “Bali Road Map” was released) represents a turning point (from slow to rapid) in the development of research on unfrozen water in frozen soil. Researchers and organizations from China and the United States are the major contributors, while Cold Regions Science and Technology is the most utilized journal for publishing research pertaining to UWFS. Currently, there is still a lack of reliable and user-friendly methods and techniques for measuring unfrozen water content. Future efforts are required to understand the mechanisms governing the magnitude of unfrozen water content and to develop new approaches to accurately and rapidly measure unfrozen water content in both laboratory and in situ.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.060 | 0.086 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".