Bibliographic record
Abstract
Knapp, Andrew. Let’s Find Momo. Quirk Books, 2017.This hide-and-seek board book is the latest from photographer, Andrew Knapp and his extremely photogenic border collie named Momo. The black and white border collie loves hiding and invites young readers to find itself and other objects. Left-hand pages list three different objects and Momo who are hidden in the corresponding right pages. Each page explores different environments along with objects that are typically found therein, but not always. For instance, in the library, readers are tasked to find a lollipop, a banana, a balloon, and Momo.Similar to Where’s Waldo, this picture book allows readers to find objects and the cute little dog that does his very best at staying hidden. The border collie takes readers on many adventures such as to a merry-go-round, gymnasium, garden, bedroom, and even a farm. With themes carefully selected, they provide a wide range of new words for young readers to learn. Each location is beautifully photographed with vibrant colours and unique angles while, at the same time, teaches new vocabulary to young children, aged 2-4. Highly Recommended: 4 out of 4 starsReviewer: Janice KungJanice Kung is a Public Services Librarian at the University of Alberta, John W. Scott Health Sciences Library. She obtained her undergraduate degree in commerce and completed her MLIS degree 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 | 0.002 |
| 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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.635 | 0.572 |
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".