The Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) Advisory Board for CMIP6
Bibliographic record
Abstract
Abstract. The Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) Advisory Board was created to provide a strong bridge between climate change applications experts and climate modelers for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). The climate change application community comprises researchers and other specialists who make use of climate information (alongside other socioeconomic and environmental information) to analyze vulnerability, impacts and adaptation of natural systems and society in relation to past, ongoing and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. The initialization of CMIP6 provided a unique opportunity to facilitate a two-way dialogue between CMIP6 climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services in order to solicit community feedback that increases applications relevance of the CMIP6 Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments thought to be of greatest importance to the climate model applications community. Climate modelers therefore received useful guidance as to the applicability and societal relevance of their simulation outputs. The VIACS Advisory Board also reflected on contributions to Obs4MIPs and user needs for the gridding and processing of model output. Furthermore, the wide application of climate model outputs by VIACS users provides an error check and ground-truthing of the climate model-based results.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.096 | 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".