The Woman in the Red Coat / The Most Extraordinary Man in the World / Forsaken / The Shape of a Smile
Bibliographic record
Abstract
I saw him drive off.I couldn't see him get into the car, but I definitely saw the vehicle move away.What kind of a man drives off like that and leaves me here?I've been wondering about that a lot.I get cold all over when I think about him.I wonder what that ride was like.Just before he drove off, I saw a red coat get into the car on the passenger's side.I could tell the coat was expensive.I didn't really get a good look at the woman in the coat.But I wonder how she came out of the building, crossed the street, saw a crowd of people gathered right by the car, the one she was aiming to get into, and didn't look beyond the crowd.She didn't look to the right.She didn't look down.She didn't look my way to see what the fuss was about.She just got into the car and he drove off right away.I wonder.Did she ask him when she got into the car?Did he answer?"Do you know what happened?"
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".