Bibliographic record
Abstract
Stevenson, Noelle, Grace Ellis, Brooke A. Allen, Shannon Watters, Maarta Laiho, and Aubrey Aiese. Lumberjanes. [Series]. Los Angeles: BOOM! Box, 2015. Print.The book I read was called Lumberjanes by Noelle Stevenson and Grace Ellis. The book was published by Boom Box in 2015. This story was about five teens trying to solve a mystery. It’s funny, crazy and a little scary. I really like the part when the Yetis were talking about the humans and being gross. Even the part when Ripley said something about the holy kitten. It would be awesome if there were more pages. I wish it was a little scarier. I would gave it a 5 out of 5 and would recommend it to all kids my age. You will love itHighly recommended: 5 out of 5 starsReviewer: NaimaMy name is Naima. I love to read fictional and nonfictional books. I like to read mystery scary and fairy tale books. I think reading is important because it’s educational.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.270 | 0.124 |
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".