Bibliographic record
Abstract
Asnong, Jocey. Nuptse & Lhotse Go to the Rockies. Victoria, BC: Rocky Mountain Books, 2014. Print.Nuptse & Lhotse are sibling cats with a sense of adventure. Finding inspiration in the Canadian Rockies, author and illustrator Jocey Asnong sends the cats on an adventure through the Canadian Rockies to help Mrs. Jasper find her missing cubs, Yoho and Kootenay. The cats along with Mrs. Jasper travel through the scenic highlights of Mountain Parks, from the Valley of the Ten Peaks to Lake Louise, along the Canadian Pacific Railway tracks to the Spiral Tunnels and up to the Columbia Icefields, with a stop to ski along the way.The story is straightforward, with simple language that works for beginner readers or reading aloud. A map at the beginning situates the events of the story, a comparison of a teddy bear to a grizzly bear is lighthearted and informative, and a maze illustration works with the plot of finding the lost cubs and is a fun activity while reading. It is the illustrations that bring the story to life by combining pencil crayon drawings with collage to create a layered visual experience leaving something new to be discovered with each read. Seamlessly incorporated into the text and illustrations are aspects of mountain geography and culture. This includes an explanation of the blue-green colour of the mountain lakes that is part of the cats’ stop in Lake Louise and homage to legendary mountain photographer Byron Harmon. These details make good entry points for further classroom learning and connect to a number of curriculum areas.The publisher, Rocky Mountain Books, is known for publications that celebrate mountain culture and Nuptse & Lhotse Go to the Rockies is an excellent addition to their growing catalogue for young readers. 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.366 | 0.191 |
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".