Bibliographic record
Abstract
As this fine documentary points out, the construction of the Alaska (originally the Alcan) Highway was one of the great achievements of twentieth-century American civil engineering, surpassed perhaps only by the building of the Panama Canal. It was not the quality of the work done that made it so remarkable, for large sections fell apart the year after it was completed, and much of it eventually had to be relocated and entirely rebuilt. What made it so impressive, as this film points out, is the speed at which it was built: over 1,500 miles of highway driven through mostly unmapped bush wilderness in the astonishingly short period of eight months. The fact that the order to build the road was issued in February 1942, that work began in April, and that a (barely) passable road was finished by the end of October must stand as one of the great achievements of the United States Army Corps of Engineers, especially because at the time the work began the corps had only a very sketchy idea of the route that was to be followed. Moreover, some of the techniques that came into play, such as building through muskeg and over permafrost, had to be worked out on site through trial and error.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.024 |
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".