Bibliographic record
Abstract
1 1 4 R S U N D O W N E R S S Y N D R O M E D A V I D B O T T O M S The last night my mother spent in Kennestone Hospital my cell rang at four, her voice raw and pleading – the nurses were trying to kill her, I needed to phone the police. Around that voice my bedroom tilted. Nurses were wheeling everyone into the basement. Thus all those hysterical gurneys clacking down the hall. Nothing would convince her otherwise – watch the elevators, listen to the screams. No one damned to the basement ever comes back. Last evening, fifty geese circled chaotically above our backyard pines, then vaguely fell into a V to break up and flag again. Something had gone haywire, shorted-out in the nervous circuitry of the world – dozens of Canada geese reeling over the suburb, tumbling, wheeling, ragging-out in a babel of figure-eights. A full two minutes before the planet leveled and sharp black lines arrowed south over pine tops not quite dark. ...
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.007 |
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".