Bibliographic record
Abstract
Savitz, Harriet May. A Girl’s Best Friend. New York: Scholastic, 1995. Print. The book I am reading is A Girl’s Best Friend. I like the book because it’s sad and funny at the same time. It’s sad because she blind. There's a blind girl who has a dog has named Jessie. She also is getting sick. And they are having a hard time with money. It’s sad and funny. It’s awesome and very sad because she is blind and the dog is getting very sick.I did not like the book when the dog might die or get killed and that she is blind so she can’t see her dog but I do love the book. If I could pick one hundred I would but I can't so I will pick five. My reason is it’s an awesome book and I like the dog, it looks cute. Highly recommended: 5 out of 5 stars Reviewer: SantanaMy name is Santana I really like reading comics because it has some action in it. I am 11 years old. My favorite things to do are dance, sing, and cook. I always go to my best friend’s house because she lives very close to me. I enjoy my reading because it is comfy and fun. I really like reading comics because it’s awesome. I also like reading real life stories and my favorite book is called A Girl’s Best Friend.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.305 | 0.282 |
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".