Bibliographic record
Abstract
Mrs. Brooks flipped the pages of her cook book back and forth. The lemon pie would be easier; but it wouldn't take much longer to make the spice cake that Martin liked. Yes, it would be the cake, she decided, because that was Martin's favorite dessert. She was a little tired of it; but, as long as Martin insisted that each cake was better than the last and that nothing else compared with it, he should have spice cake, all he wanted of it, every week, all summer too. September would come soon enough when he would have to go back north to that little college close to the Canadian border. Teaching physics year after year there where the snow covered the ground most of the winter could, she suspected, become monotonous, for each spring in June when Martin arrived at his sister's farm home he would dash upstairs two steps at a time to change suit and dignity for overalls and a happy grin. Fun loving Martin! He was not only her favorite brother, but her son's favorite uncle too. Bill was probably out with Martin now. She would have to see that he came back in time to take his music lesson. After the cake was in the oven, she would go find them.
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 | 0.003 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.802 | 0.601 |
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".