Bridging Time Scales: Evaluating Natural Analogues and their Role in Long-Term Corrosion Prediction
Bibliographic record
Abstract
Predicting long-term material degradation due to corrosion, particularly over decades to centuries, has been a long standing scientific and engineering challenge. Atmospheric corrosion is a complex electrochemical process influenced by dynamic environmental factors such as cyclic humidity, temperature fluctuations, and varied pollutant concentrations. Accelerated Corrosion Testing (ACT) is employed to be able to provide rapid results, however ACT often fails to replicate the nuanced conditions of corrosion, leading to different degradation mechanisms and poor correlation with actual outdoor performance. This thesis report analyzes the case study of silver (Ag) tarnishing to determine how comparable real-time atmospheric corrosion and ACT are. The use of mineralogical ores as a natural analogue and a toolkit of advanced analytical microscopy techniques are combined to develop a more accurate and confident understanding of material degradation across all relevant timescales.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".