Bibliographic record
Abstract
Abstract Corrosion monitoring refers to corrosion measurements performed under industrial or practical operating conditions. This requirement has led to the evolution of corrosion monitoring tools toward real‐time data acquisition, process control tools, knowledge based systems, and smart structures. Correct and effective corrosion monitoring strategies should be used as a proactive tool to assist with operating a plant or any other system more effectively, thereby prolonging its life and gaining optimum throughput. Fundamentally, four strategies have been discussed to an organization in its dealings with corrosion. Corrosion monitoring systems vary significantly in complexity, from simple coupon exposures or handheld data loggers to fully integrated plant process surveillance units with remote data access and data management capabilities. Numerous real‐time corrosion monitoring programs in diverse branches of industry have revealed that the severity of corrosion damage is rarely uniform with time. Rather, serious corrosion damage is usually sustained in time frames where operational parameters have suffered upsets. These undesirable operational windows can only be identified with the real‐time monitoring approach. An example of a widely available technique is linear polarization resistance, which many commercial monitoring systems use to measure corrosion rates.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.039 |
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".