Bibliographic record
Abstract
Smallman, Steve. Poo in the Zoo. Illus. Ada Grey. Wilton, CT : Tiger Tales, 2015. Print.This story is a fun way to introduce kids to the fact that there all sorts of messy but very necessary jobs in the world. “There’s too much poo in the zoo!” for zookeeper Bob McGrew. Children who are at the age when all things scatological are both fascinating and hilarious will revel in watching Bob clean up after all of the animals in the zoo. The creative wordplay, rhyme scheme, as well as the fact that the word “poo” shows up an average of 2 or 3 times a page is sure to delight and amuse young listeners:“There was tiger poo, lion poo, prickly porcupine poo, Plummeting giraffe poo that landed with a splat.Gobs of gnu poo, bouncy kangaroo poo,A dotted line of droppings from a fat wombat!”After consuming some fireflies, an escaped iguana produces a green, glowing, alien-like pile of poop which draws crowds to the zoo. Hector Glue, who owns a travelling side-show of exotic poo, arrives on the scene to acquire it for his collection. With the money from the sale, the zookeeper is able to buy a robot “pooper scooper”. Ada Grey’s illustrations are rendered in a bright colour palette punctuated by lots of interesting textures. For example, in the scene where we meet Hector Glue, there is a myriad of patterns used in the depiction of the animal coats and skin as well as in the clothing of the zoo patrons, particularly in Hector’s Victorian-era showman outfit. Children will enjoy perusing the bottles of exotic poo from Hector’s collection which are also reproduced in the endpapers. There are clever gems such as: “Squirrel Poo: Warning May Contain Nuts”. Among the fictional creatures mentioned on the bottles, there are also many lesser-known animals such as an ocelot and a blob fish. This provides an opportunity for teachers or parents to encourage young readers to learn more about these animals. “Poo in the Zoo” could also work as a complement to a lesson or non-fiction book on animals or on caring for pets, provided that the teacher or parent points out the differences between fact and fiction. Recommended for ages 3-7.Recommended: 3 out of 4 starsReviewer: Kim FrailKim is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. Children’s literature is a big part of her world at work and at home. She also enjoys gardening, renovating and keeping up with her kids.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.358 | 0.296 |
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".