Monitoring Corrosion in Aging Systems - New Possibilities and Old Fundamentals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Over the past decade, relatively little change in corrosion monitoring techniques has occurred, as far as the fundamental measurement parameters are concerned. Noteworthy additions include the introduction and commercialization of more sensitive derivatives of Electrical Resistance (ER) sensors and applications of fiber optic technologies to corrosion sensing. However, remarkably rapid change is apparent in the sensors and instrumentation available for performing the fundamental corrosion measurements and also in the manner in which fundamental data is captured, processed, interpreted, stored and converted into useful information. The introduction of new technological corrosion monitoring features may lead to an element of “hype” and the risk of neglecting sound “old” principles, established over many decades of industrial corrosion monitoring practice. A brief review of the “old” fundamentals is therefore provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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