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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".