New applications for an EMIC coupled to an atmospheric chemistry model - The University of Victoria Earth system climate model version 2.10 + FAIR chemistry module
Bibliographic record
Abstract
As an Earth system model of intermediate complexity (EMIC), the University of Victoria Earth system climate model (UVic-ESCM) has a comparably low computational cost (4.5–11.5 h per 100 years on a simple desktop computer). It is therefore a well-suited tool to perform experiments that are not yet computationally feasible in a state-of-the-art Earth system model. For example, the UVic-ESCM can be used to perform large perturbed parameter ensembles to constrain uncertainties, but also run a multitude of scenarios while at the same time simulating a well resolved carbon cycle. Thanks to its representation of many important components of the carbon cycle and the physical climate and its ability to simulate dynamic interactions between them, the UVic-ESCM is additionally a more comprehensive tool for process level uncertainty assessment compared to integrated assessment models (IAMs). The coupling of this EMIC with an atmospheric chemistry module based on the FAIR simple climate model, now allows to directly implement GHG emission files as an input to the model, which makes it a valuable tool for many ‘what-if’ questions about climate turnaround times. Especially in the context of assessing the carbon cycle responses to future long-term climate change scenarios including e.g. marine CDR or terrestrial CDR implementations. In this presentation we will introduce this new model setup and show examples of first applications of this novel tool, while showcasing the advantages that it brings about.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".