A CLOSER EXAMINATION OF THE CLAIMS OF EXCELLENT CORROSION RESISTANCE OF STAINLESS STEEL: TEXTBOOK MISCONCEPTIONS AND MISINTERPRETATIONS
Bibliographic record
Abstract
This paper examines misconceptions and misinterpretations concerning the common assertion in textbooks that stainless steels has excellent corrosion resistance due to the addition of alloying elements such as nickel (Ni), chromium (Cr), and molybdenum (Mo).A closer look at this claim reveals underlying assumptions that lead to this imprecise statement. Corrosion experiments have been established in a course in engineering materials that expose these assumptions.Following a discussion of the tests and their results, it is suggested that statements in textbooks that stainless steel has excellent corrosion resistance should be qualified. It is hoped that by bringing this shortcoming to the attention of engineering educators, the misconceptions and misinterpretations can be corrected.
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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 | 0.028 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".