Bibliographic record
Abstract
Going through some old papers in the attic of our family farm near Dundalk, Ont., I came across this handwritten recipe among other, less puzzling, instructions for making apple dumplings, buttermilk cake and dandelion wine. It dates, I would guess, from the first or second decade of the 20th century. For the kidneys 1 pt. good gin 2 oz. sweet nitre 1 oz. oil of juniper 1/2 oz. turpentine 1 good stick of horseradish grated fine. Shake them up well. Take a wineglass 3/4 parts full 3 times a day. The 1946 edition of Stedman's Medical Dictionary gives the following definitions: Sweet nitre. Spiritus aetheris nitrosi: an alcoholic solution of ethyl nitrate, aldehyde and other substances. A sedative, diuretic and diaphoretic in doses of 20–30 min. [ A minum is 1/60th of a fluid drachm: practically speaking, a drop.] Oil of juniper berries. A volatile oil distilled from the fruit of Juniperus communis. A carminative, diuretic and stimulant in doses of 5–15 min. There is no entry for turpentine.
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) | 0.001 | 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 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.878 | 0.739 |
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".