A hairy problem.
Bibliographic record
Abstract
An 18-year-old woman presented to our outpatient clinic three days after her return from Playa del Carmen, near Cancun, Mexico, where she had spent her 14-day holiday in a luxury beach resort. She did not have any sexual encounters during her stay. On the last day of her visit, during the first days of her menses, she had anxiously observed small moving objects in her menses secretions in the bath tub sink after washing herself in the hotel bathroom. To ensure the moving, living objects were not residing in the bath, she had cleaned the bath and washed herself again. Once more she observed one or two small black curling and twitching creatures. In the days prior to these observations she had not experienced any itching, local skin abnormalities or vaginal discharge. Notably, she had caught one organism from the bath and stored it in a contact lens container. After her return to the Netherlands she visited her general practitioner, who, after a non-revelatory internal medical examination, referred her to the our travel clinic. Upon physical examination the patient did not show any abnormalities. Visualisation of the deceased organism under a dissection microscope revealed a blackish wormlike insect larva with multiple body segments and rings, protruding hairs and clearly defined head and tail, 6 mm in length and 0.5 mm in width (
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".