Marley and the Great Easter Egg Hunt
Bibliographic record
Abstract
Marley is at it again! The lovable Labrador can't seem to stay out of trouble, and the trip to the town Easter egg hunt is no exception. When Marley and his girl Cassie arrive at the town square, they find it bustling with Easter cheer and decorations. The hunt is about to begin! When Marley hears that the winner must find the biggest, most special egg, he is determined. He will be the one to win. Sniffing from place to place, his nose directs him to several Easter eggs. Sadly, other children take them before he has the chance. Discouraged, he turns to Cassie, who suggests they find another place to look. The pair barrel through the town, crashing through stores and bakeries, but they can't find the egg. The Easter egg hunt comes to an end. Marley, disappointed, turns back to the gathered crowd. Luck is on his side though, and there's one more place to look. Marley can be a winner after all!
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.011 |
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".