[download pdf] Disney and Pixar's Turning Red: 4*Town 4*Real: The Manga by Dirchansky, KAIfee
Bibliographic record
Abstract
Disney and Pixar's Turning Red: 4*Town 4*Real: The Manga by Dirchansky, KAIfee Download Book ➡ Link Read Book Online ➡ Link Disney and Pixar's Turning Red: 4*Town 4*Real: The Manga Dirchansky, KAIfee Page: 176 Format: pdf, ePub, mobi, fb2 ISBN: 9781974734795 Publisher: VIZ Media LLC Read full books online for free without downloading Disney and Pixar's Turning Red: 4*Town 4*Real: The Manga 9781974734795 ePub CHM by Dirchansky, KAIfee (English Edition) Overview Spend the day with the members of 4*Town in this manga companion to the hit Disney and Pixar film Turning Red! Spend the day with the members of 4*Town in this manga companion to the hit Disney and Pixar film Turning Red! 4☆Townies are hyped to see 4☆Town performing their favorite hits live, but how will Canada’s greatest boy band spend the day leading up to their epic Toronto concert? Hang with Robaire, Jesse, Tae Young, Aaron T., and Aaron Z. as they enjoy a rare break in their busy schedules. Jesse and Tae Young embrace their artistic side and visit a ceramics museum, Aaron T. gets his fashion on at the mall, and Aaron Z. and Robaire stay in the dance studio to master their moves before the sold-out show! Only the realest fans deserve to get this up close and personal with Tween Beat magazine’s hottest band of the year!
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.514 | 0.305 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".