Hot trends and impact in permafrost science
Bibliographic record
Abstract
Abstract An increased interest in Arctic environments, mainly due to climate change, has changed the conditions for permafrost research in recent years. This change has been accompanied by a global increase in scientific publications, as well as a trend towards open access publications. We have analyzed abstracts, titles and keywords for publications on permafrost from 1998 to 2017 to identify developments (topics, impact and collaboration) in the field of permafrost research in light of these changes. Furthermore, to understand how scientists build on and are inspired by each other's work, we have (a) developed citation networks from scientific publications on permafrost and (b) conducted an online survey on inspiration in permafrost science. Our results show an almost 400% increase in publications containing the word permafrost in the title, keywords or abstract over the study period, and a strong increase in climate‐change‐related research in terms of publications and citations. Survey respondents (n = 122) find inspiration not only in scientific journal publications, but to a large extent in books and public outreach materials. We argue that this increase in global‐scope issues (i.e., climate change) complementing core permafrost research has provided new incentives for international collaborations and wider communication efforts.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".