Bibliographic record
Abstract
CUAJ's Editor-in-Chief role is in the capable hands of Dr. Rob Siemens, going into his second term.Establishing CUAJ with Dr. Klotz and Dr. Valiquette at the very beginning and completing my term with Dr. Siemens has been the highlight of my professional career so far.As I move on, the lessons I've learned at the CUA, personal and professional, will remain with me and guide my steps moving forward.CUAJ experiences the many challenges faced by medical journals, including lack of resources, lack of advertising revenues to cover the costs of running the journal, and late reviewers.I think, however, that CUAJ has a stronger hand than most; it has the support of CUA members and Board of Directors.CUAJ has become more than just a journal for peer-reviewed articles.It has become a home for Canadian urologic research, a home for residents to share their experiences and challenges, and a home for both academic and community urologists to add to the growing body of urologic literature.CUAJ will be left in good hands -with Denise Toner continuing to be the robust
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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.086 | 0.062 |
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".