Progress and Trends in Coupled Model Intercomparison Project (CMIP) Research: A Bibliometric Analysis
Bibliographic record
Abstract
Understanding of the Coupled Model Intercomparison Project (CMIP) and its research progress and applications is critical to answer scientific questions related to climate change. While numerous scientific papers based on CMIP have been published, there is no quantitative study examining scientific research on climate variability, predictability, and change supported by CMIP. Therefore, the statistical characteristics of CMIP-related publications, including journals, disciplines, co-occurrence and burst detection of keywords, and bibliographic coupling, were analyzed using bibliometric analysis. The results show that research based on CMIP has increased exponentially from 2000 to 2023. About 20% of the research was published in the Journal of Climate and Climate Dynamics. CMIP-related research spanned several disciplines, including meteorology, atmospheric science, geosciences, and environmental sciences. The United States, China, and the United Kingdom ranked top three for CMIP publications. The prominent focus of related research involved the whole climate system, including climate change and variability, climate behavior, the carbon cycle, sea surface temperature, sea ice, modeling, bias correction, simulations, climate sensitivity, extreme events, soil moisture, hydrology, and future change. This study can help relevant scientists better understand the developments and trends of CMIP research, thereby facilitating the use of CMIP data.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.197 |
| Science and technology studies | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".