Bibliographic record
Abstract
Abstract The development of effective methods for directly measuring liquid metal quality, prior to casting and final solidification, has long been a goal for Process Metallurgists. For aluminum, which is generally much cleaner than steel, it is first necessary to concentrate the inclusions by filtering the metal through a porous frit, before then freezing the remaining metal, and subjecting it to microscopic examination (e.g. PoDFA). An alternative method is to take a sample of metal, freeze it, and then dissolve the metal to release the particles (inclusions) through elutriation (the Slime Technique). The only true on-line, in-situ , methods are the Ultrasonic Liquid Metal Sensors (such as the Mansfield Molten Metal Sensor), and the Electric Sensing Zone Methods (such as LiMCA and ESZ-pas). Currently, perhaps the most reliable, but least satisfying, technique is to wait for customer complaints to identify problems. JFE has developed an ultrasonic, on-line, system that registers larger inclusion clusters in rolled steel sheets as they are produced. Alternatively, many steelmakers will use PDA (Pulse Discrimination Analysis) on a small surface of solid steel, to arrive at conclusions concerning inclusions less than 10 microns. Unfortunately, this ignores the much larger inclusions normally present within a steel melt that are responsible for compromising metal properties. The late Professor Iwase was a strong believer in the development of good techniques and methods to monitor and control metallurgical processes, including those related to metal quality. This review is dedicated to his memory, and to his strength of perseverance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".