Navigating the Jungle of CMIP Data as a First-Time User: Key Challenges and Future Directions 
Bibliographic record
Abstract
Output generated by the different phases of the Coupled Model Intercomparison Project (CMIP) has underpinned countless scientific projects and serves as the foundation of the United Nations climate change reports. While initially CMIP was largely driven by the scientific curiosity in the broader climate modeling community, CMIP output has also become a crucial data source for disciplines more tangentially related to physical climate science such as the economic modelling community. The upcoming CMIP phase 7 is expected to produce the largest amount of CMIP-related data to date. However, with an increasing number of modelling systems, represented realms, model complexity, variable names, experiments, and different grid types, the initial exposure to CMIP output has undoubtedly become an overwhelming experience for first-time users. For this presentation, we would like to start a conversation with users who are in or have recent experience of being in the early stages of employing CMIP outputs for their research, and together identify:Key barriers and challenges experienced when first using CMIP data Additional documentation/tools needed to facilitate the use of CMIP data Key pieces of advice for new CMIP users
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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 | 0.118 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.037 | 0.054 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.023 |
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".