Making the Case for Geometric Identifiability in Electrochemical Impedance Spectroscopy
Why this work is in the frame
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Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a prevalent technique for battery characterization and testing. Despite recent advancements, EIS optimization remains a challenge faced by conventional EIS sampling techniques. One such example is the redundancy of samples. For instance, additional excitation frequencies are used to identify the parameters of a given equivalent circuit model (ECM), while this can be done with fewer excitation frequencies from a geometrical standpoint. In this brief, fundamentals of geometric identifiability of EIS ECMs are discussed and showcased for a framework formulated based on Fisher information matrix (FIM) optimization sampling. By using the geometric identifiability analysis, it is shown that the classic FIM EIS, optimizing the sensitivity of likelihood function to all the unknown parameters, leads to redundant samples. A geometric FIM EIS is then proposed, which reduces the number of sampling regions without compromising the estimation accuracy. It is yet shown that both the classic and the proposed geometric FIM EIS methods are suboptimal with respect to the geometric identifiability analysis. Both ordinary-order and factional-order ECMs are discussed, and simulation results are provided and compared against that of a conventional EIS implementation with uniform sampling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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