Bibliographic record
Abstract
A fellow goes into his local veterinarian and says “Dr. — I've got a world famous champion horse, but I can't get the birds to stop nesting in his mane — what should I do?” The veterinarian says, “Very simple — pick up a box of yeast on your way home, sprinkle it in your horses mane, and the birds will go away.” The horse owner does as he's told, buys a box of yeast on the way home, sprinkles it in his horse's mane, and instantly all the birds fly away never to return! The horse owner is ecstatic, rushes back to the veterinarian, and says, “It's a miracle Dr. — your recommendation worked after every other treatment I could think of had failed but tell me, what is the rationale for this strange treatment?” The veterinarian says, “Very simple — yeast is yeast and nest is nest, and never the mane shall tweet.” Apologies to Rudyard Kipling (Submitted by Dr. Tom Sanderson, Maxwell, Ontario)
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.844 | 0.716 |
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".