Bibliographic record
Abstract
Patterson, James. Maximum Ride: Angel. New York: Little Brown, 2011. Print. In "Angel", Max and her flock are 98% Human and 2% Bird, so they can fly. The flock's names are Fang, Nudge, Gazzy, Angel, and Iggy. They were all created in a lab and called "Experiments". There is also another type of Experiments called an "Eraser". They are half Human and half Wolf. Jeb helped Max and the flock escape the school but then disappeared for 2 years. Max and the Flock stay in a safe house Jeb got them in the middle of nowhere. Since Fang disappeared to go start a gang and Dylan promises Max to fight by her side and keep her safe since Fang is gone. Dylan is a clone of some other Dylan that died somehow and he isn't a part of the Flock. How Max met her half-sister Ella was Max and Fang were looking for Angel so then Max hears screaming and she goes to investigate and leaves Fang so when she gets there this guy is yelling at Ella and Max stops and she tells him to stop and he tries to hurt her but she is dodging the attacks so Max kicks him into the car and he grabs a shot gun and shoots her in the shoulder near her wing is. Then Fang comes down and takes care of the guy and Ella takes Max to her mom’s house to save her and when she wakes up Fang is outside and she sees Dr. Martinez which is Ella's mom who patches up Max's shoulder and Max Seems to have a tracking chip inside her ribcage that was planted inside her when she was little. I rate this book a 5 because it is the best book series ever.Highly recommended: 5 out of 5 stars Reviewer: Shanyce My name is Shanyce. I am 11 years old. I like to read James Patterson and Zac and Heather Brewer books. My favorite book from James Patterson is the Maximum Ride series and the Witch and Wizard Manga series. My favorite book from Heather/Zac Brewer is The Chronicles of Vladimir Tod Series. Right now I'm reading "Angel" by James Patterson.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.448 | 0.389 |
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".