Bibliographic record
Abstract
Adventurer, writer, and artist Allen Smutylo has experienced some of the wildest and most captivating waters imaginable in all corners of the globe. The stories in The Memory of Water —all of them accompanied by the author’s own stunning artwork—describe his adventures in the Arctic, South Pacific, Great Lakes region, and India. In the Arctic he is attacked by a polar bear, stalked by a rogue walrus, and nearly drowns in ferocious waters. But his Arctic stories also celebrate human creativity as they recount the life of the pre-Inuit people, who, hunting in a changing environment, endured many hardships and developed new technologies, such as the sea kayak, to cope. Other stories include an account of a sojourn in a small Georgian Bay fishing village as a young artist, an adventure on an urban river in southwestern Ontario, and a portrayal of the complex underwater world of the South Pacific. Travelling the River Ganges in India, the author finds that a massive misuse of water is complicated by a billion people’s faith-based adoration of the same water. The Memory of Water probes a crucial and contemporary issue—that of our relationship to water and the wildlife and human life that depends upon it. This book will appeal to anyone interested in the natural world, in artistic depictions of it, or in a good story well told.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".