Hotspots and trends in frozen soils research in 2010–2019
Bibliographic record
Abstract
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.062 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".