Bibliographic record
Abstract
Hoena, Blake. Everything Dinosaurs. Illus. Franco Tempesta. Washington: National Geographic Society, 2014. Print.This colourful, glossy and magazine-like title in the National Geographic Kids’ Everything series will please almost any young would-be paleontologist. Written specifically for the 8-12 year old audience, it is chock full of photographs, images, facts, maps and activities expertly compiled by a large team of National Geographic staff. It has boldly designed graphics and as a high-interest non-fiction title, will appeal to reluctant readers.Children will enjoy the appealing images, beautifully created by artist and illustrator Franco Tempesta who specializes in naturalistic illustration, and in particular, realistic images of dinosaurs and prehistoric mammals. Included are “Explorer’s Corners,” information from the field from an expert. In this case, University of Chicago professor Paul Sereno, who in his photograph and cartoon image looks a lot like Indiana Jones! Real photographs of fossilized dino eggs, meteorites, dinosaur theme parks and paleontologists add a touch of authenticity. Especially fun are the infographics and quizzes, on topics ranging from how dinosaur names are chosen, dinosaurs in Hollywood films and the ‘rock stars’ of the paleontological world.As with other titles in the series, Everything Dinosaurs contains a table of contents, diagrams, definitions and an index. This title and the series will appeal to upper middle and upper elementary readers interested in non-fiction. It would be a fine addition to elementary school libraries and public libraries.Recommended: 3 stars out of 4 Reviewer: Debbie FeisstDebbie 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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.291 | 0.278 |
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".