Bibliographic record
Abstract
This code simulates an outbreak of COVID-19 and uses a dual control algorithm, iterative linear quadratic regulator (iLQG), to minimize an objective function through actively learning the system's uncertain parameters. This is a set of MATLAB scripts that run the dual iLQG algorithm. Data was derived from the following sources: Li and Todorov (2007): Iterative linearization methods for approximately optimal control and estimation of non-linear stochastic system Tassa, Mansard, Todorov (2014): Control-limited differential dynamic programming Kohler et. al (2021): Robust and optimal predictive control of the COVID-19 outbreak These files require MATLAB R2021b or newer and the simulation can be run from the main file Main_Outer_Control_loop.m. Files include: Main_Outer_Control_loop.m iLQG_function.m l_cost.m Measurement.m ContinuousStateDynamics.m DiscreteStateDynamics.m forward_pass.m backward_pass.m Todorov_estimator.m SPKF_function.m simulate_system.m finite_difference.m boxQP.m makePD.m pp.m sabs.m
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.367 |
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; both teacher heads agree on what is shown here.
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".