Comparison of non-destructive flaw detection methods in PM
Bibliographic record
Abstract
The kinetics of refinement of Fe-Si alloys by acid leaching is affected by their structural state. Two different phase constitutions were studied to access this effect: an industrial alloy with a low AlCa ratio and a laboratory-prepared alloy with a high Al content. The first contains silicon, FeSi2 (tet.), FeSi2 (orth.), beyond a quaternary phase Fe-Al-Si-Ca (Caalsifer), CaAi2Si1.5, CaSi2, Al-Fe-Si as minor phases. The precipitation of Al-Fe-Si phases is induced in the second by the high AlCa ratio. In a previous study, leaching experiments by a two-step procedure have shown that Ca-Al-Si, Caalsifer and Al-Fe-Si phases are more soluble. The Cracking Shrinking Model (CSM) was applied to explain the behaviour of the industrial alloy. In the present study, it is shown that the same model applies to the kinetics of dissolution of the laboratory-prepared alloy and also, that an adequate control of FeAl vs. SiCa ratios may improve the attainable refining yield.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".