Bibliographic record
Abstract
Kraulis, Julie. An Armadillo in Paris. Toronto: Tundra Books, 2014. Print.This picture book explores the beautiful city of Paris through the eyes of Arlo, the armadillo. Arlo’s grandfather wrote travel journals for him to use once he was old enough to travel and he goes on an adventure to Paris in search of the Iron Lady. The story intricately weaves passages from the journal and Arlo’s personal experiences as he explores the city. Each stop brings him closer to his final destination and provides clues on what is the Iron Lady. Some of Arlo’s experiences include buying macaroons from a French bakery, observing the pyramid in the courtyard of the Louvre, and visiting the Jardin du Luxembourg or Luxembourg Gardens. At the end of his journey, Arlo discovers that the Iron Lady is the iconic Eiffel Tower. Beautiful artwork accompanies the narrative. Overall, this book takes readers on an adventure through Paris that is suitable for children from Kindergarten to Grade 2. The book concludes with a page “All about the Iron Lady,” which includes interesting facts about the Eiffel Tower that will delight children and adults. Recommended: 3 out of 4 starsReviewer: Janice KungJanice Kung is an Academic Library Intern at the University of Alberta’s John W. Scott Health Sciences Library. She obtained her undergraduate degree in commerce and completed her MLIS in 2013. She believes that the best thing to beat the winter blues is to cuddle up on a couch and lose oneself in a good book.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.037 |
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".