Bibliographic record
Abstract
Holm, Jennifer L, Matthew Holm, and Lark Pien. Sunny Side Up. New York, NY: Graphix- Scholastic, 2015. Print.The name of the book is Sunny side up. The authors are Jennifer and Matthew Holm and it was published in 2015 by Scholastic Inc. Sunny got sent off to Florida to live with her grandfather for the summer. Everyday her grandfather said that he had something fun but it was always so boring. In the place where Sunny's grandfather lived there was only old people but there was another kid with their dad to visit his name was Buzz.Things I liked/don't like I liked when Sunny went into the pond and Buzz yelled ''BIG AL'' it was so funny. Big Al is an alligator he's big and dark green he lives in the golf pond. I didn’t like how Sunny's grandfather lied to her about smoking. She found out that he smoked because she found a cigarette packs everywhere in the car, bathroom and the cereal.My rating for the book is 5/5 I would recommend this book to age 10-15 because it’s kind of hard for kids age 7-9 or lower it has words that they won’t understand but for older kids they will understand. Highly Recommended: 5 out of 5 starsReviewer: LanitaMy name is Lanita. My favorite place to read is in my grandma’s room with the fan on. My favorite book is Sunny side up its so fun to read. I would recommend this book to my grandma because she loves reading books and she will probably like this book. Reading is important because it could show you words you never seen before it could level up your education.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.400 | 0.299 |
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".