Role of Solution Resistance in Measurements from Atmospheric Corrosivity Sensors
Bibliographic record
Abstract
Abstract The measurement of corrosion rates by various electrochemical methods in conductive solutions has a sound theoretical basis. In many practical cases, the influence of solution resistance is negligible compared to the polarization resistance of the electrodes. However, where these techniques are applied to measuring atmospheric corrosivity, the role of ionic resistance between electrodes seems to be under estimated. The influence of solution resistance for four types of atmospheric corrosivity sensors was analyzed on the basis of their equivalent electrical circuits. These included time-of-wetness, galvanic, linear polarization resistance, and electrochemical noise sensors. Analysis of the equivalent circuits indicated that solution resistance plays a confounding role in interpreting apparent corrosivity measurements. Therefore the effects of changes in solution resistance must be accounted for in order to properly interpret apparent corrosivity measurements. One proposed solution is to manufacture corrosivity sensors that have a constant ionic resistance between the electrodes. This may be possible with a material that functions as a solid-state ionic conductor. Another proposed solution is to measure solution resistance in parallel with the apparent corrosivity. Depending on the type of sensor, the true corrosivity can then be calculated from the apparent corrosivity and solution resistance according to an appropriate model.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".