Bibliographic record
Abstract
Travel with Bob Baxter, editor of the tattoo magazine, Skin & Ink, on his two-week solo journey through the small towns and big cites of Americas Pacific Northwest. From the Oregon-California line, northward through Washington State and over the border to Vancouver, B.C., every day is a new adventure, as Bob meets the exotic women and lusty men who proclaim their individuality in tattoos and the artists who create them. Meet the living legends of the art:Terry Tweed and Dave Shore, Pete Stevens and Don Deaton, John the Dutchman and Vyvyn Lazonga. Talk with the Worlds Most-Tattooed Woman, Krystyne Kolorful. Check out the new wave of ink sinkers, including Anchor Tattoo, Electro-Ladylux, Tiger Lily, and Lucky Dog. And say a graveside good-bye to Bert Grimm, the man who tattooed Bonnie and Clyde. Armed with only his camera, a laptop computer, and a box of chocolate chip energy bars, Bob Baxter sets out to discover the tattoo pulse of the Pacific Northwest. Meet many of the great artists in the mysterious shops and secret backrooms of this, the most exciting art movement since the Renaissance!
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.644 | 0.444 |
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".