TU‐F‐137‐01: Writing and Reviewing Papers for Medical Physics
Bibliographic record
Abstract
Writing and Reviewing Papers for Medical Physics There is an art to writing a scientific paper so that it communicates accurately, succinctly, and comprehensively. Developing this art comes with experience, and sharing that experience with younger physicists is an obligation of senior scientists, especially those with editorial responsibilities for the journal. In this workshop, the preparation of a scientific manuscript will be dissected so participants can appreciate how each part is developed and then assembled into a complete paper. Then the review process for the paper will be discussed, including how to examine a paper and write an insightful and constructive review. Finally, we will consider the challenge of accommodating the concerns and recommendations of a reviewer in preparing a revision of the paper. A second feature of the workshop will be a discussion of the process of electronic submission of a paper for consideration by Medical Physics. The web‐based PeerX‐Press engine for manuscript submission and management will be examined, with attention to special features such as epaps and line‐referencing. Finally, new features of Medical Physics will be explained, such as Vision 20/20 manuscripts, Physics Letters and the standardized formatting of book reviews. Learning Objectives: 1. Improve the participants' abilities to write a scientific manuscript. 2. Understand the review process for Medical Physics manuscripts and how to participate in and benefit from it. 3. Appreciate the many features of the PeerX‐Press electronic management process for Medical Physics manuscripts. 4. Develop a knowledge of new features of Medical Physics.
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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 | 0.079 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.157 | 0.309 |
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".