Train wreck victims (Olmo), 1956
Bibliographic record
Abstract
8 images. Train wreck victims (Olmo), 23 January 1956. George Muelinberg (injured); Richard Everline (injured); Henrietta Muelinberg (injured); Mrs. Irene Miller (helping with victims); Miss Mary Patricia Brame (injured); Eva Thomas (injured); Alberta Nelson (injured); Blanche Magee (helping with injured).; Caption slip reads: "Photographer: Olmo. Date: 1956-01-23. Reporter: Thackrey. Assignment: Wreck victims. 61 & 62: Eva Thomas of Santa Ana, tells of the bouncing and swaying of the train just before it jumped the tracks. Her husband, Warren, was in the wreck with her at General. 64: Alberta Nelson of Santa Ana sips water from a glass held by Blanche Magee, admission room superintendent at General Hospital. She said she didn't want her friends to know she was in the wreck at General. 65 & 66: George Muelinberg of Sorrente, who has a broken left leg, lights a cigarette for Richard Everline of San Diego, whose left arm was fractured in the train wreck at General. 63: Henrietta Muelinberg of Sorrento, tells us how she and her husband, George, climbed out of the wreck after their coach overturned. Her injuries reported slight, fractured left shoulder. 67: Mary Patricia Brame. 68: Mrs. Irene Miller of San Diego gives a sip of orange juice to Miss Mary Patricia Brame, also of San Diego. Mrs. Miller is the mother of Miss Brame's employer at a San Diego dance studio. They wound up in the same room at Good Sam".
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.441 | 0.129 |
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".