Bibliographic record
Abstract
Sayres, Brianna K. Where Do Diggers Sleep at Night? Illus. Christian Slade. New York: Random House, 2012. Print.If the title Where Do Diggers Sleep at Night? seems a tad familiar, well, it probably is. In the same vein as the ultra-popular Good Night, Good Night, Construction Site, Diggers presents the sweet nighttime rituals of diggers, trucks and other heavy machinery. At first I thought this was a simple effort to take advantage of a similar, bestselling title however Sayres’ work does indeed hold its own.In this picture book aimed at ages 3-6, first time picture book author Sayres gives young heavy equipment aficionados a delightful take on the bedtime story. In rhyming couplets and often in a humorous manner, all sorts of trucks, cranes and tractors get ready for rest under the watchful headlamps of their caregivers: “Where do garbage trucks sleep / when they’re done collecting trash? / Do their dads sniff their load and say, / ‘Pee-yew—time to take a bath’?” Sure to get the young ones giggling.The sleepy-eyed dozers and tow trucks eventually give way to an equally sleepy young boy in his cozy bed, with a reminder that the trucks will be waiting for him when he wakes. Save for one naughty truck, winking, under the bed (my five-year-old happily pointed this out).Though the illustrations by former Disney animator Christian Slade are rather cartoon-like and not realistic, they match the text well, are in soothing nighttime colours and allow for the trucks to have droopy eyes and smiling faces. Read in a lyrical fashion, or even as a song, this would be a nice end to any wee truck lover’s day. This would make a nice addition to any public library or as a gift.Reviewer: Debbie Feisst Recommended: 3 stars out of 4Debbie is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. When not renovating, she enjoys travel, fitness and young adult fiction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".