Bibliographic record
Abstract
Journal publications play a key role in the dissemination of research findings in the biomedical acoustics community. However, authors are often unsure of how to write a paper that will be considered as excellent by their peers, because the notion of excellence is arguably subjective and ill-defined. To demystify such an issue, this presentation will discuss four major elements (novelty, significance, rigor, writing quality) that are often found in excellent journal papers in biomedical ultrasound. Since 2022, these four elements have been adopted as the peer review criteria by the flagship ultrasonics journal of the IEEE press. Multi-day writing workshops with interactive, hands-on tutorials have been designed to engage prospective authors on how to plan and prepare journal manuscripts with the four elements of excellence. Reviewer training workshops have also been offered to inform journal reviewers about these evaluation criteria and expectations. Through these initiatives, we aim to establish a quality-driven publishing emphasis within the biomedical acoustics community. Achieving so will have long-term benefits on the reputation and growth of this research field.
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 | 0.100 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.034 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".