Bibliographic record
Abstract
Donnelly, Jennifer. Deep Blue. Glendale: Circle Seven, 2014. Print. This book is about mermaids. All the mermaids wanted to cast a spell on the octopus and the mom didn’t want the spell to happen. I like the mermaids because they have a tail that they can swim with and they have a king.I like to read this book because it’s easy to read. Some of the words are harder than others though. Sometimes its hard read and you get mixed up with the words. Deep Blue is a good book so far but I have only read chapter one. I don’t like Storm and the Empress. I would rate this book as 4.5. Highly recommended: 4.5 out of 5 stars Reviewer: Kaylin I am Kaylin and I like to read any kind of book. I like chips and candies and I like to ride my bike. Every day I go to the park. I like to read the newspaper and short books because it is easy to read. I want to read but it’s kind of hard sometimes. It has interesting stories about our world.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.338 | 0.273 |
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".