Bibliographic record
Abstract
In January 2017, physics professor Hart Bezner was driving home to Waterloo, Ontario, from the remote Arctic hamlet of Tuktoyaktuk when he turned onto the lonely, 724 km Dempster Highway. Outside temperatures hovered around –40 °C. But Bezner, who was wearing gloves, a hat, and an electric jacket plugged into the car’s 12 V outlet, remembers feeling “supremely comfortable.” He was listening to satellite radio and admiring the starlit sky. Suddenly, Bezner noticed that the car’s heater was blowing cold air, though a gauge showed that the engine’s temperature was climbing. He stopped, opened the hood, and loosened the cap of the radiator—the system that regulates engine temperature. He recalls a “geyser” of steam and liquid knocking the cap out of his hand and into the darkness. Below it, however, the radiator seemed to have frozen. This prevented coolant from circulating, which caused the engine to overheat. Bezner slowly drove
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".