Analyzing Electrochemical Noise Data with Time-Domain Techniques
Bibliographic record
Abstract
Abstract This article introduces two techniques for characterizing individual transients in electrochemical noise (EN) data in the time domain. These approaches extract information from only electrochemical transients in EN data, i.e. where current and potential are correlated. An algorithm is introduced for identifying transients based on locating records where both the current and potential derivatives are simultaneously zero. A measure of the intensity of the transient was obtained from the current amplitude near the transient apex. A measure of the uniform corrosion rate of the less active electrode during a transient was obtained from the ratio of differences in potential and current during a single transient. The corrosion rates thus obtained appear to have a lognormal distribution. These techniques were illustrated from laboratory measurements for three systems, i.e. carbon steel (UNS G10100) electrodes in saline and two magnesium alloys, AZ91D (UNS M11916) and ZA1040 (experimental alloy), in saline/magnesium hydroxide solutions. This approach seems to be readily transportable to a field monitoring system as long as the sampling frequency is high enough and there is a processor available to convert raw data as it is collected and store only the processed results.
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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 | 0.000 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".