Bibliographic record
Abstract
RecipeFiona grew up in Allentown-in Gram's and Pop's one-story, red brick home on Bradford Street across from AL To Fiona he was uncle though, and still to me, a genemion younger.When I required Gram as a babysiner, Mondays and Fridays, and sometimes days in between, A1 would stop by for tea in the mid-summer afternoons-his worn, lime green and pastel blue plaid shorts that alternated with only a single pair of khakis, creases ever visible down the center of each leg, front and back His bead, always neatly combed, never surpassing the quarter of an inch below his chin.And his hat, forest green and netted in the back, plush in the front-almost foamlike, embroidered with the Knights of Columbus logo.Al never hocked for tea.He quietly let himself in, passing through the rear porch, even then leaning heavily on a cane, up the concrete steps to the unlocked back door.The porch k enclosed in glass, a small wooden plaque with three hooks beneath hanging from the wall to the left of the door.Hand-etched into the plaque are the words uhlnik fate.A longer wooden scroll of stained birch
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.801 | 0.614 |
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".