Bibliographic record
Abstract
Stilton, Geronimo. The Treasure of Easter Island. New York: Scholastic, 2013. Print.What happens in the book is Geronimo's sister Thea went to Easter Island to look for treasure on it. She got captured by pirates. Thea, one of the characters, met Professor Von Dusty Fur, he is an archaeologist who specializes in ancient treasure. Then Geronimo and his friends, Wild Willy and Susie Shutter mouse, trap his cousin. They all fell down a hole and landed right under the Rano Kau Volcano. After a pirate took them out of their cage then tied them up behind crates full of gold coins. I like to read this book because it has a sense of adventure and fun. Treasure Island would be a fun place to go to some day and explore. I rate this book as a 5 out of 5. Highly recommended: 5 out of 5 stars Reviewer: JoshuaMy name is Joshua and what I like to read is fictional books. Some books I like to read are by Geronimo Stilton or the book Spirit Animals. I enjoy reading because I can keep my mind busy and it is fun reading about stories that could be true but aren't.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".