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Record W4362576745 · doi:10.1002/ppp.2186

Hotspots and trends in frozen soils research in 2010–2019

2023· article· en· W4362576745 on OpenAlexaboutno aff
Wenhao Liu, Ren Li, Xiaoqian Shi, Tonghua Wu, Xiao Wu

Bibliographic record

VenuePermafrost and Periglacial Processes · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaState Key Laboratory of Cryospheric ScienceNational Natural Science Foundation of China
KeywordsPermafrostTundraSoil waterContext (archaeology)Physical geographyArcticClimate changePlateau (mathematics)TaigaCarbon cycleEnvironmental scienceBorealChinaEarth scienceGeologyGeographyEcosystemSoil scienceArchaeologyForestryEcologyOceanography

Abstract

fetched live from OpenAlex

Abstract In the context of climate change, research on frozen soils has attracted much attention in recent years, and numerous research papers have been published on these topics in the last decade. However, the present status and developmental trends in frozen soils research have not been reported systematically. Herein, a bibliometric analysis was conducted using 7,108 research papers on frozen soils published between 2010 and 2019. The results indicate that: (a) although the number of articles published increased from 432 in 2010 to 1,066 in 2019, the average number of citations per paper reached a maximum of 5.40 in 2014, and subsequently decreased to 2.99 in 2019; (b) China, the USA, and Canada ranked first to third in terms of total papers; (c) the most popular author keywords were boreal, tundra, Landsat, lakes, decomposition, dissolved organic carbon, permafrost thaw, and carbon cycle; and (d) the five most popular research topics in 2010–2019 were the characteristics and factors influencing frozen soils, the Arctic carbon cycle under the background of its complex environment, permafrost changes on the Qinghai–Tibet Plateau in the context of climate change, ancient frozen soils in various historical periods, and frozen soils in the Arctic.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.114
GPT teacher head0.343
Teacher spread0.229 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2023
Admission routes1
Has abstractyes

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