Bibliographic record
Abstract
This paper describes the modeling process in a simulation study of compressed air energy storage for the large power system in Ontario, Canada. The study is fully documented in a White Paper from NRStor Inc, the storage developer. This paper focuses on the participatory modeling process. It began with discussions within the NRStor team, followed by meetings with power systems managers and staff. The participating stakeholders grew in number, eventually represented generating companies, the system operator, regulators, energy ministry staff and advocates for utility customers and for the environment. Most of the stakeholders were accustomed to computer modeling, as the agencies used dozens of highly detailed models. But the stakeholders were not accustomed to the system dynamics approach to tie the pieces of system together, nor were they accustomed to the participatory discussions which were encouraged by speed and clarity in the modeling. The methods to achieve speed and clarity will be illustrated at the conference with live demonstrations. The key to speed was the use of separate, but interconnected models for short-term operations along side the model of long-term trends. The key to clarity was an interface designed for (1) clear graphical displays of results in formats familiar to stakeholders and (2) convenient input controls and navigation to allow instant responses to participants suggestions. The stakeholders had many suggestions to expand and improve the model. The improvements were quickly implemented, and new meetings followed shortly thereafter. New simulations would trigger more discussions and more requests for further improvements, with stakeholders particularly interested in adding CO2 emissions to the model. The modeling process led to increased understanding for both groups: the development team gained a better understanding of the power system, and the agency participants gained new understanding of the value of compressed air energy storage and the best strategy to sustain Ontario’s success in limiting CO2 emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.129 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".