Update on an Information System for High Temperature Corrosion
Bibliographic record
Abstract
Abstract This presentation summarizes the efforts to develop and expand a comprehensive information system for corrosion of metals and alloys in high temperature gases. The current data collection represents about 6.4 million hours of exposure time for about 4,900 tests with 80 alloys. Data are being generated at the rate of about one million exposure hours per year. The system manages/exploits corrosion data from well-defined exposures and determines corrosion product stabilities. New insights in the analysis of thermochemical data for the Fe-Ni-Cr-Co-C-O-S-N system are being compiled. All known phases based upon any combination of the elements are being analyzed to allow the most complete and accurate assessments of corrosion product stabilities. Use of these data will allow prediction of corrosion product stabilities, which can be used to deduce the likely corrosion mechanism. The program has uses in corrosion research, alloy development, failure analysis, lifetime prediction, and process operations. The corrosion mechanisms emphasized are oxidation, sulfidation, sulfidation/oxidation, and carburization.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.033 |
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".