Bibliographic record
Abstract
Litchfield, David. The Bear and the Piano. Clarion Books, 2016.In this folio sized volume, David Litchfield presents a charming story and some amazing artwork. You first notice the light in the forest, where the bear finds a piano and begins teaching himself to play. Then as you follow his career as a concert pianist, you see the extraordinary light in the city, with a spotlight on his picture outside the theatre, above the marquee that reads “The Bear and the Piano.” Inside Litchfield captures the light and dark of an old tiered theatre, with the bear in a tuxedo seated at a grand piano. The music messages in the story are as charming as the images. Although the bear is very successful as a concert pianist, he misses the forest and his friends. When he returns home, he discovers that his friends have not forgotten him and when he plays for them he knows that they are “the most important audience of all.” The text and the images work well together. This book should be included in school libraries and public libraries.Highly recommended: 4 stars out of 4Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".