Bibliographic record
Abstract
Asnong, Jocey. Nuptse & Lhotse Go to Iceland. Victoria, BC: Rocky Mountain Books, 2015. Print.The sibling cats, Nuptse & Lhotse, are back for a saga inspired adventure to Iceland. Author / Illustrator Jocey Asnong creates a story that mirrors the form of 10th and 11th century Icelandic sagas. Characters are named in the saga tradition, Thorse the Galloper mimics Erik the Red, and the narrative arch incorporates challenges the cats must complete before coming to the end of their saga and return a Viking helmet to Katla the Fire-breather. The challenges pay tribute to many of the unique aspects of Icelandic culture, language, animals, and geography, creating a story that is as educational as it is entertaining. Nuptse & Lhotse Go to Iceland, like the previous book Nuptse & Lhotse Go to the Rockies, uses illustrations rich with extra details to supplement the text of the story. A map at the beginning sets the stage for the tour of Iceland the cats take while decoding runic letters. The characters and places are situated using the unique visual images of the land and culture of Iceland. For instance, the character of Lopi the Sweatermaker is introduced sitting on a hill made of balls of yarn, by a distinctive cliffs of the Snæfellsnes peninsula, with Lopapeysa sweaters strung about; the same sweaters are worn by characters throughout the book. There is even a sly reference to Canada’s Icelandic connections in a Huldufolk named Gimli.Nuptse & Lhotse Go to Iceland is an excellent book to introduce children to Iceland and teach them a little bit about the language, culture, and geography unique to the deceptively named island.Highly recommended: 4 out of 4 starsReviewer: Lauren WheelerLauren Wheeler is a Program Lead at the Alberta Museums Association. When not assisting museums across Alberta, Lauren likes to explore and relax in her hometown of Canmore.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.257 | 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".