The Open Science approach of the Intergovernmental Panel on Climate Change (IPCC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.022 | 0.039 |
| 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 it