Bibliographic record
Abstract
TenNapel, Doug. Bad Island. New York: Scholastic, 2011. Print.This book is about a mother, father, big brother and a little sister. They make a plan to go on a boating trip and the son doesn't want to go. He tried to stay home but his dad said no. So he just went with them. While they were boating, a storm started. It got so bad that their boat started crashing into waves and the boat sunk while they were in it. They all passed out and woke up on an unexplainable island, where they were scared because there were weird noises on the island. So the dad started to make a shelter with dead trees. They saw weird marks on rocks, so they started thinking there was someone or something on the island. The next day it was sunny and nice but at night they saw creatures. Weird creatures, like aliens! They had to survive! I would recommend this book because it's a fun adventure type of novel and I enjoyed reading it.Recommended Reviewer: Eben My name is Eben. I am 13 and I love to SKATE. Skate or Die!
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.234 | 0.133 |
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".