Lightening the performance burden of individual‐based models through dimensional analysis and scale modeling
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
Abstract While individual‐based models are attractive for some modeling problems, the lengthy times required for simulating large populations can impose high opportunity costs by limiting model comprehension, refinement and user interaction. This paper demonstrates a novel technique for using dimensional analysis and scale modeling to reduce the performance barriers associated with individual‐based model simulation. Given a dimensionally homogeneous simulation model with a large population, we propose a rigorous, systematic and general‐purpose technique to formulate a “reduced‐scale” individual‐based model that simulates a smaller population. Outputs of the reduced‐scale models can be precisely transformed to yield results representative of a full‐scale model—without the need to run the full‐scale model. While discretization effects and heterogeneity limit the degree of scaling that can be achieved, these techniques are notable in relying only upon dimensional homogeneity of the full‐scale model, and not on the specifics of model behavior or use of a particular mathematical framework. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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