Future Needs for Tribo-Corrosion Research and Testing
Bibliographic record
Abstract
Tribo-corrosion is an emerging interdisciplinary subject that spans from basic research on the behavior of surfaces in mechanical contact in chemically active surroundings to the test methods needed to quantify its effects, and from the selection of materials for bio-implants to the minimization of surface degradation and wastage in advanced energy conversion systems. Such a diverse field brings with it many challenges in understanding, testing, standardization, and application to engineering practice. This paper summarizes a panel discussion and participant survey held at the Third International Symposium on Tribo-Corrosion in Atlanta, Georgia, USA, in April 2012. It reflects a sense of agreement on many of the key scientific challenges in the field and the fact that tribo-corrosion is still in its infancy in terms of broad industry recognition, education, and the ability of those who conduct tribo-corrosion research to connect their laboratory results and theories to applications. Some sub-fields, notably the bio-tribo-corrosion of medical implants, have witnessed active international research efforts, but the engineering community in many other important areas of technology may not yet be aware of the field despite numerous tribo-corrosion problems that may exist within their purview.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.029 |
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".