Bibliographic record
Abstract
Landers, Ace, and Dave White. Anakin to the Rescue! New York: Scholastic, 2012. Print.I chose to read the Lego Star Wars book called Anakin to the Rescue. The book was written by Ace Landers and published by Scholastic Inc in 2012.The book that I read was about when Anakin Skywalker and Obi Wan Kenobi are assigned to protect senator Amidala. They are led into a much deeper mystery searching for answers.I liked this book because it was easy to read and it was funny. The pictures were drawn very well and the story was easy to follow.I didn't like the part when Obi Wan got captured because he got distracted by cookies. Jedi don't get distracted by cookies.I would give this book a rating of 3 out of 5. I would recommend it to students in grades 2 or 3 because it is easy to read but funny and entertaining.Recommended: 3 out of 5 starsReviewer: NathanielMy name is Nathaniel. I like to read anime or manga books the most. My favourite place to read is either at home or at school. I like to read because you learn new things from books.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.259 | 0.198 |
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".