Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The SEED technology, a rheocasting process based on the slurry-on-demand approach, is an emerging technology that was developed in the mid-2000s. Many publications with regard to the process and to alloy development using this technology were made since, and several industrial units are operated worldwide. Moreover, the process is still actively developed and is fully supported by a team of scientists, engineers and technicians. With a global objective of addressing the user requirements and the industry needs, works were conducted toward optimization of the process and equipment. At first, the focus was on developing a simplified version of the SEED process to eliminate the so-called “drainage” phase while preserving the prime quality of the slurry produced. Improvement of some system components and integration of new features were also targeted to secure the overall equipment efficiency (OEE) and increase the process reliability. This work, backed with the optimization of process parameters and comprehensive techniques adapted for semi solid casting, led to the consolidation and even improvement of the properties of the parts produced for the common foundry alloys 356/357 and 319. Furthermore, the non-drained SEED version was applied to the validation of the process capabilities for uncommon cast alloys with works on 6061 wrought alloy, Duralcan metal matrix composite, and others. The results confirmed that the SEED process can efficiently be used in non-drained mode and achieve the same quality of slurry as the drained version originally developed. It is now proven in the industrial scale and actually integrated in the updated industrial equipment. Moreover, the capabilities of the process for special alloys and applications are still the subject of active development works.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it