Bibliographic record
Abstract
A friend told me recently that he is suffering from crisisfatigue. Open an issue of just about any medical jour-nal, he said, and you will find at least one article warning of an impending critical shortage of professionals. This issue of JOGC is no exception; on page 598, the Maternal Fetal Medicine Committee of the SOGC draws our attention—quite correctly—to the looming MFM shortage in this country.1 Unfortunately, the MFM sub-specialists are not alone in facing potential shortages. Over the past couple of years, we have become increasingly aware of shortages of other sub-specialists (oncologists, urogynaecologists, and REI subspecialists), with no evi-dence that these shortages will resolve. The SOGC has repeatedly drawn our attention to the now inevitable short-age of maternity care providers and to Canada’s decline in standing among OECD countries in rates of infant and
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".