Bibliographic record
Abstract
In Strand, travel writer and amateur naturalist Bonnie Henderson traces the stories of wrack washed up on the mile-long stretch of Oregon beach she has walked regularly for more than a decade. Henderson's writing conveys both a keen attention to the specifics of place and an expansive field of vision. The burned hull of a long-abandoned fishing boat, a glass fishing float, the egg case of a skate, a beached minke whale, an unusual number of dead murres, and an athletic shoe are the starting points for essays that reach across the globe. Henderson takes readers from Coos Bay, Oregon, to Vancouver, B.C.; from the currents circulating through the North Pacific to the Eastern Garbage Patch? between Hawaii and California; from China's Shenzhen Special Economic Zone to fishing villages on the coast of Hokkaido, Japan.As Henderson uncovers these odysseys, she meditates on current issues, events, and phenomenaoil spills, the proliferation of ocean debris, international trade, the evolution of sharks, and the survival prospects of whales. The characters that emerge range from the world's leading minke whale researchers to the crew of a Coast Guard airbase to a small-town salvager of wrecked fishing boats, glued to the radio and praying for disaster. Strand offers a thoughtful look at the surprisingly far-ranging journeys of what washes up on our Pacific shores.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 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".