Bibliographic record
Abstract
I always liked strawberries in pastries, in jam, or diced with mint and mixed with sparkling lemonade. Grapefruit was another favorite, topped with brown sugar and baked in halves in my third-floor apartment. Familiar and comforting, what they represented evolved with my medical training. Strawberries evoked a characteristic oral finding—swollen, hot, and alarming. Grapefruits were a stand-in for patient deltoids during vaccination training, where I learned what happens if I try to remove a needle cap like a marker. Medical education presented different contexts to ordinary objects. Safety pins became tools in the neurological exam; white vinegar became a treatment for otitis externa; bananas became models for suturing practice. I cannot forget the resemblance of strawberry to tongue nor grapefruit to shoulders on my weekly stroll down the grocery aisles. A still life presents a motionless arrangement of objects, usually fruit, flowers, fabric, and glassware. My digital artwork Still Life, on the cover of this issue, reflects the piecewise integration of specialized objects with the everyday—medical equipment interspersed with fruit. The composition invites the viewer to consider the relationships of the subjects. The soft, diffuse style highlights the ambiguity in knowledge acquisition and development, and allows the bright fruits to stand out against the blue-dominant medical equipment. In composing this still life, I could quietly step back, consider these juxtapositions with newfound curiosity, and get to know them again.Still Life
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.295 | 0.114 |
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".