The Open Science approach of the Intergovernmental Panel on Climate Change (IPCC)
Bibliographic record
Abstract
The introduction of Open Science and FAIR data practices into the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has enhanced the transparency of its results. The approach focuses on the figure generation process and the traceability of figures within the reports. It makes the report contents more visible and accessible to scientists and users, fostering reusability and subsequent scientific and technical progress. Challenges lie in the scale, the number of figures, and thevariety of a sometimes very complex data analysis used to generate figures. In addition, the authors, organized into chapters, have many different ways of working that need to be taken into account when framing the data documentation requirements for authors. The contribution introduces IPCC’s revised approach to Open Science for the current Seventh Assessment Report (AR7; Stockhause et al., 2024), highlighting the importance of principles like transparency, FAIR data and TRUSTworthy repositories, but also the high value of collaborating with external partners in the climate and data sciences, e.g. WCRP Coupled Model Intercomparison Project (CMIP) and Coordinated Regional Climate Downscaling Experiment (CORDEX), Research Data Alliance(RDA) and the geosciences unions EGU/AGU/JpGU.
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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 | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Open science Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.101 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".