Bibliographic record
Abstract
The Confréérie mondiale de les chevaliers de l'omelette gééante (World Fraternity of Knights of the Giant Omelet) spreads good cheer in several countries of the French-speaking world through the making and sharing of giant omelets. The tradition began in 1973 in Bessièères, France, where the townspeople fed indigent families in town. The practice of making a giant omelet soon spread to Frééjus, France; Malmedy, Belgium, and other cities with francophone histories: Dumbééa, New Caledonia; Granby, Quebec, and Abbeville, Louisiana. Each omelet has a regional flavor. The Louisiana omelet is influenced by Cajun and Creole cooking traditions. In Belgium, the omelet is made with pork lard. The recipe for Granby's more traditional omelet is familiar to most Quebecers: eggs, salt, parsley, thyme, pepper, scallions, and olive oil. In this case: fifteen thousand eggs, four liters of salt, five thousand scallions, and twenty liters of extra-virgin olive oil. The omelet is calculated to serve ten thousand.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.520 | 0.461 |
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".