Open-source modelling infrastructure: Building decarbonization capacity in Canada
Bibliographic record
Abstract
Actions that transform our energy system are the cornerstone of decarbonizing our economy but have been hindered by the ineffective interface between researchers and decision-makers in Canada. This paper begins by arguing for a more holistic perspective on energy system decarbonization modelling and exploring how insights can aid evidence-based decision making. We then respond with the development of a modelling platform that includes three core pillars: (1) a toolbox of models that together represent the integrated energy system, (2) a dataset containing the inputs required to populate those models, and (3) a visualization suite to analyze and communicate their outputs. The Spine Toolbox is leveraged to process these three components in an efficient workflow. Taken together, the platform promotes the usability of model results by fostering consistency, transparency, and timeliness. Furthermore, the epistemic limitations of energy systems modelling and implications for platform and model design, and engaging extended peer communities, are discussed. Our hope is that this platform can be a foundational resource that facilitates collaboration between energy system and decarbonization researchers, modelling teams and decision-makers, ultimately enabling the effective application of evidence-based policy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".