Bibliometric analysis on machine learning in climate change article during ten years
Bibliographic record
Abstract
This research used bibliometric method to calculate scientific productivity of corresponding author, first author, affiliation, and correspondence country for machine learning in climate change articles on SCOPUS database from 2015 to 2024. Moreover, spatial simulation of country output is displayed by geographic information system, showing distribution of scientific productivity in each country on the world. Total 4,406 articles are analyzed and simulated, they indicated that research productivity has increasing trend and increases sharply from 2021 to 2024 year with 528 to 1239 articles. Y. Wang correspondence author is 1st ranking and the most research output with 19 articles, including 12 articles in China, 1 article in Finland, and 6 articles in United States. Almost authors publish strongly from 2022 to 2024 with high output as Y. Wang, J. Li, Y. Li, J. Yin, and J. Chen correspondence authors. Y. Zhang first author has the most scientific output with 16 articles and 1st ranking, concluding 13 articles publish in China, 2 articles in Canada, and 1 article in Singapore. Publication of affiliation increases strongly from 2021 to 2024 year and Department of Civil Engineering has the most publication with 97 articles in 20 countries, 1st ranking. China has the most publication in 2015-2024 with 1148 articles, 1st ranking in correspondence author. Publication in countries is increased strongly from 2020 to 2024 year and from 2021-2024, the whole countries have publication at all.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.069 | 0.104 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".