The Life of Permafrost: A History of Frozen Earth in Russian and Soviet Science, by Pey-Yi Chu
Bibliographic record
Abstract
During the Second World War, military installations were rapidly built in Alaska—still a sparsely populated territory—to protect against Japanese incursions. Aware that their Soviet allies had much experience in cold climate construction (the majority of the earth’s permafrost was in the USSR), the United States Geological Service (USGS) commissioned Siemon Muller, a Stanford geology professor who had immigrated from Russia, to write a literature review of Russian and Soviet publications on frozen earth. He produced Permafrost or Permanently Frozen Ground and Related Engineering Problems (1945), in which he invented the English neologism ‘permafrost’ which was basically a translation of the Russian term, vechnaia merzlota (eternally frozen earth) (p. 128). A decade later, as the chill of the Cold War settled in and the United States had both military and extractive reasons to pursue extensive construction projects in the Arctic, another Soviet émigré in the employ of the USGS, Inna Poiré, criticised the term permafrost in a 1953 commissioned report. She wrote, ‘the feature is neither permafrost, nor perennially, nor eternally, nor vechnaia frozen ground; the chief subject is not the ground itself but groundwater; and finally, it is not yet clear, what to consider as frozen ground, or as frost in ground’ (p. 6). Thus, she pinpointed the problem at the centre of Pey-Yi Chu’s excellent book.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".