Bibliographic record
Abstract
Weiner, Andrew. Down by the River: A Family Fly Fishing Story. Harry N. Abrams, 2018. This is a beautifully illustrated book that tells a simple story about a boy, Art, who goes on a fly-fishing trip with his mother and grandfather. The story recalls a time when Art’s grandfather taught his mother to fish. April Chu has used a subdued palette for her two-page riverine landscapes, with lots of green and rich autumn colours in the environment. The book has a calm and peaceful feel about it that mirrors the contemplative nature of fly-fishing. The text is simple and descriptive of a day spent on the river. The reading level is too difficult for the intended Kindergarten to Grade 2 audience, so an adult will need to read the book aloud, especially those sentences that could confuse young readers with difficult concepts or complicated jargon: “The line arced forward and the fly landed softly a few feet above the rock. It drifted with the current past the rock. There was a splash and the line went tight.” The last three pages contain information about fly fishing, the clothing worn by fly fishers and where to get more information about the sport. The end pages are decorated with images of intricate flys with such fun and mysterious names as: “Ian’s Crunch Caddis,” “Black Fur Ant,” and “Purple Parachute Adams.” This book is a good introduction to fly fishing for younger children that also tells a charming story. Highly recommended for school and public libraries. Highly Recommended: 4 stars out of 4 Reviewer: Sandy Campbell Sandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".