Editorial to celebrate the 3rd anniversary of Synthesis and Sintering
Bibliographic record
Abstract
Serving as a member of the editorial board of Synthesis and Sintering and the founder of the Synsint Research Group is undoubtedly a great honor for me. I am happy to witness the significant growth of this journal as a global platform that publishes scientific articles with high standards. As we celebrate the third anniversary of Synthesis and Sintering, we are proud to present to readers a collection of high-quality articles written by leading experts and young researchers in the fields of synthesis and sintering. These articles, which have been published regularly in the past three years, have shown the commitment of the journal and its management team to the development of science and research in these fields. In this editorial article, the goals of the journal, its achievements and our prospects are presented.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".