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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.024 |
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".