Bibliometric analysis of studies of the Arctic and Antarctic polynya
Bibliographic record
Abstract
Based on the polar polynya-related 1,677 publications derived from the Web of Science from 1980 to 2021, this study analyses the scientific performance of polar polynya research with respect to publication outputs, scientific categories, journals, productive countries and partnerships, co-cited references, bibliographic documents and the thermal trends of keywords. The number of publications and citations on polar polynya has increased 17.28 and 11.22% annually since the 1990s, respectively, and those numbers for Antarctic polynya have surpassed that of the Arctic polynya since 2014. Oceanography, geosciences multidisciplinary, and environmental sciences were the top 3 scientific categories in the Arctic and Antarctic polynya research field. Nevertheless, ecology and meteorology are gaining ground in the Arctic and the Antarctic recently. The Journal of Geophysical Research-Oceans accommodated most publications for both polar regions, followed by Deep-Sea Research Part II-Topical Studies in Oceanography and Polar Biology. The Continental Shelf Research and Ocean Modeling were favored journals in Arctic and Antarctic polynya research, respectively. The USA dominated the polar polynya study field with 31.74%/43.60% publications on the Arctic/Antarctic polynya research, followed by Canada (40.23%/4.32%) and Germany (17.21%/11.22%). Besides, Australia occupied the second most popular position in the Antarctic polynya research. The keywords analysis concluded that the polynya topics that generated the most interest were altered from model to climate change in the Arctic and ocean water and glacier in the Antarctic over time. This study gives a summary of the polar polynya scientific field through bibliometric analysis which may provide reference for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.079 | 0.321 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".