Seeing Jesus in a piece of toast and other scientific discoveries win Ig Nobel awards
Bibliographic record
Abstract
“Have you seen Jesus’ face in a piece of toast? Elvis in a tortilla? Your Uncle Bob in the clouds? It’s perfectly normal if you see non-existent objects,” said Kang Lee of the University of Toronto, accepting his Ig Nobel award for neuroscience on 18 September at Harvard University in Cambridge, Massachusetts. Lee and colleagues from China and Canada were among the 10 winners of Ig Nobel awards announced at the 24th annual ceremony and handed out by winners of the proper Nobel prizes. The theme this year was “Food.” The awards are organized by the lighthearted journal Annals of Improbable Research and its editor, Marc Abraham. The prizes reward studies that “first make people laugh and then make them think.” The awards were a plastic tray with utensils such as those in an airplane meal, a document signed by real Nobel laureates, and a monetary prize—a 10 trillion dollar Zimbabwean note. The celebration this year included a mini-opera, “What’s Eating You,” modeled on Don Giovanni; two blizzards of paper airplanes; an 8 year old girl who loudly interrupted participants who spoke longer than one minute; and lectures by experts who explained their subjects first in 24 seconds and then in seven words. Lee told The BMJ that he and colleagues often encountered people who said they saw Jesus’s face on a piece of toast and wondered whether the people were deluded or whether there …
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.038 |
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".