Bibliographic record
Abstract
Meisel, Paul. Good Night, Bat! : Good Morning, Squirrel! Highlights, 2016.Paul Meisel, Geisel Honor award winning author and illustrator, creates a humorous story about friendship and miscommunication. This fictional picture book follows homeless Bat on his journey to finding the perfect new home. Through whimsical mis-read notes the friendship between Bat and Squirrel blossoms.The literary content in this story is invaluable for young readers. Meisel demonstrates the complexities of the English language by playing with simple words and phrases while demanding that readers also read the images. This play on words offers a charming world in which young children can explore and expand upon their vocabulary.Complimenting this hilarious story are illustrations that demand the reader’s attention. At a first glance, the visuals appear gloomy due to the brown, green and grey tones. However, the expressions of Bat and Squirrel, along with the simple but easily misunderstood leaf note’s enable young readers to become enthralled in the world of Bat and Squirrel.Combining the two essential features of playing with language and reading illustrations, Good Night, Bat! Good Morning, Squirrel! is an essential read-aloud story for any early childhood classroom.Highly Recommended: 4 out of 4 starsReviewer: Leah Den HaanLeah Den Haan is a grade one French immersion teacher with Edmonton Public Schools. She has always enjoyed children’s literature and loves sharing her love of reading with her students on a daily basis.
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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.251 | 0.252 |
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".