Spicy Curries and Cups of Tea: Dining Along the Darjeeling Himalayan Line
Bibliographic record
Abstract
All aboard for a delicious ride on nine legendary railway journeys! Meals associated with train travel have been an important ingredient of railway history for more than a century—from dinners in dining cars to lunches at station buffets and foods purchased from platform vendors. For many travelers, the experience of eating on a railway journey is often a highlight of the trip, a major part of the “romance of the rails.” A delight for rail enthusiasts, foodies, and armchair travelers alike, Food on the Move serves up the culinary history of these famous journeys on five continents, from the earliest days of rail travel to the present. Chapters invite us to table for the haute cuisine of the elegant dining carriages on the Orient Express; the classic American feast of steak-and-eggs on the Santa Fe Super Chief; and home-cooked regional foods along the Trans-Siberian tracks. We eat our way across Canada’s vast interior and Australia’s spectacular and colorful Outback; grab an infamous “British railway sandwich” to munch on the Flying Scotsman; snack on spicy samosas on the Darjeeling Himalayan Toy Train; dine at high speed on Japan’s bullet train, the Shinkansen; and sip South African wines in a Blue Train—a luxury lounge-car featuring windows of glass fused with gold dust. Written by eight authors who have traveled on those legendary lines, these chapters include recipes from the dining cars and station eateries, taken from historical menus and contributed by contemporary chefs, as well as a bounty of illustrations. A toothsome commingling of dinner triangles and train whistles, this collection is a veritable feast of meals on the move.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".