Bibliographic record
Abstract
my patient just died and i am now outside the or and i hear the family crying behind the white walls and moments before my white hands had held her white hands and i kept saying you are going to breathe and you are going to breathe and you are going to breathe and when i was 16 i got into a horrible accident that still stutters my step and the impact i felt then of the glass cutting new words into my mouth and the pavement rouging my skin was less than the pressure i crushed into her failing chest for she was going to breathe and she hugged me before the operation with softness and she said she was nervous like a newborn cloud on a summer’s day and to calm her down i showed her a picture of my cat who breathed a meow and i transitioned to my dog who breathed a bark and she laughed with all the breath that had ever been breathed in this tiny terrifying place and she asked me if my pets would love to run around like mad in this hospital and i am now outside the or where my patient just died and the lights ooze into the night and the mop weeps onto the floor and some torn miscellaneous hairs curl on the cradle of my scrubs and a palm sinks into my shoulder to tell me that i did a good job assisting the code and by the way one of my patients from the early morning wants to talk about increasing their pain medications and i pass by the family still crying and crying and crying and i stutter back home with my shadow lagging behind only to see a cat and dog there patiently sitting, silently breathing, waiting to run madly alive outside.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.217 | 0.116 |
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".