Bibliographic record
Abstract
Ever wanted to do one-stop shopping for online medical journals? A Prince Edward Island family physician has anticipated the need by creating My Morning Journal (www.mymorningjournal.com), a handy site that provides links to journals famous and obscure. Creator Garth Slysz came to his interest in the Web and computers honestly enough — his father was director of the Computer Science Department at Mount Allison University in New Brunswick — and he decided to combine it with his love of family medicine. “After 11 years in practice, I had collected many medical journal bookmarks, but it was a little tiresome looking to see if new editions were available, so I wrote a Java program that told me which ones were new. It worked well and I thought other doctors might enjoy this.” His site provides links to journals ranging from well-known titles such as the Lancet to top specialty journals such as Chest and obscure ones such as the Turkish Journal of Gastroenterology. Links to more than 120 journals are provided, with colour coding indicating which ones have been published that day or that week. Slysz launched his site last June and said he has had positive responses from around the world. “My hope is that physicians will use this page to help keep up with all the new medical information. The page makes it available in smaller doses and saves much time in looking around for new articles.” Why did he do it? “Satisfaction, and it helps me keep up on my journals. No fame and fortune yet, though.”
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.606 | 0.571 |
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".