MétaCan
Menu
Back to cohort
Record W3133628245 · doi:10.3390/w13050708

A Scientometric Review of Research Status on Unfrozen Soil Water

2021· review· en· W3133628245 on OpenAlexaff
Shuna Feng, Huan Zhang, Jialong Lv, Miles Dyck, Qingbai Wu, Hailong He

Bibliographic record

VenueWater · 2021
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Alberta
FundersHigher Education Discipline Innovation ProjectState Key Laboratory of Frozen Soil EngineeringNorthwest A and F UniversityChina Postdoctoral Science FoundationNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsEnvironmental scienceWater contentSoil waterScientometricsSoil scienceComputer scienceEngineeringLibrary scienceGeotechnical engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.911
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0660.009

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.

Opus teacher head0.294
GPT teacher head0.428
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWaterSame topicClimate change and permafrostFrench-language works237,207