Bibliographic record
Abstract
Explores the history and present-day reality of grain elevators on the Great Lakes. Winner of the 2017 Gertrude H. Dyke Award presented by the Thunder Bay Historical Museum Society Winner of the 2017 Ernest R. Zimmerman First Publication Award presented by the Thunder Bay Historical Museum Society Grain Dust Dreams tells the story of terminal grain elevators-concrete colossi that stand in the middle of a deep river of grain that they lift, sort, and send on. From their invention in Buffalo, New York, through their present-day operation in Thunder Bay, Ontario, David W. Tarbet examines the difficulties and dangers of working in a grain elevator-showing how they operate and describing the effects that the grain trade has on the lives of individuals and cities. As Tarbet shows, the impact of these impressive concrete structures even extends beyond their working lives. Buildings that were created for a commercial purpose had a surprising and unintended cultural consequence. European modernist architects were taken by the size and elegance of American concrete elevators and used them as models for a revolution in architecture. When the St. Lawrence Seaway made it possible for large ships to bypass Buffalo, many Buffalo elevators were abandoned. Tarbet describes how these empty elevators are now being transformed into centers for artistic and athletic performance, and into a hub for technical innovation. Buffalo has found a way to incorporate its unused elevators into the life of the city long after the grain dust from them has ceased to fly.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".