Estimating parameters of S-systems by an auxiliary function guided coordinate descent method
Why this work is in the frame
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Bibliographic record
Abstract
The S-system, a set of nonlinear ordinary differential equations and derived from the generalized mass action law, is an effective model to describe various biological systems. Parameters in S-systems have significant biological meanings, yet difficult to be estimated because of the nonlinearity and complexity of the model. Given time series biological data, its parameter estimation turns out to be a nonlinear optimization problem. A novel method, auxiliary function guided coordinate descent, is proposed in this paper to solve the optimization problem by cyclically optimizing every parameter. In each iteration, only one parameter value is updated and it proves that the objective function keeps nonincreasing during the iterations. The updating rules in each iteration is simple and efficient. Based on this idea, two algorithms are developed to estimate the S-systems for two different constraint situations. The performances of algorithms are studied in several simulation examples. The results demonstrate the effectiveness of the proposed method.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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