Bibliographic record
Abstract
Acrumpled and broken strand of asphalt rises at the northern edge of Ulan-Ude, wanders through the dark woods of the Khamar-Daban Mountains, and finally settles into a band of fertile bottom land in a narrow stretch of coastal plain approaching the eastern shore of Lake Baikal. A rattly old Toyota van skitters along the road, passing lonely farms and tiny villages that gather up out of nowhere and disappear just as quickly, domed churches that seem miles from any worshipers, and an occasional solitary babushka by the side of the road selling whatever she’s been able to squeeze from the earth or gather in the woods. There are seven of us riding this highway on this raw morning in October of 2000, crammed into the van and bobbing like buoys to its irregular rhythms—James and me from Boston, our guide Andrei Suknev, his colleague Igor and our driver Kim, all from the city of Ulan-Ude, and two young women who have also signed on with Andrei for a few days—Elisa, from France, and Chanda, from Canada. We’re all eating pine nuts that we bought from one of those women at a wide spot in the road—they’re called orekhi here—and washing them down with lemon soda from a huge plastic bottle. Andrei is showing us how to crack open the nuts’ hard shells with our front teeth and excavate their soft and pungent meat with our tongues. At an austere restaurant in a tiny village that Andrei tells us is called “Noisy Place,” we eat a lunch of rice and some sort of meat, dry bread, and a peculiar variation on borshch, and we pee in an outhouse across the road. We get back in the van and rumble on. We’re heading for a remote national park on Baikal’s eastern shore, but at the moment I’m not quite sure where we’re going. I’d asked Andrei to take us hiking and camping on the lakeshore, to introduce us to local residents, communities, and culture. He’s promised to do that, but he hasn’t provided much beyond the barest details, and none of us has been asking for more.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.050 |
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".