Frozen in Time: Permafrost and Engineering Problems, by Siemon W. Muller
Bibliographic record
Abstract
REVIEWS • 477the author has a successful record of producing other photography books recording his world travels, and I expect that more are coming.the endorsements included in the preface by caroline Alexander (author of several international best-sellers, including The Endurance: Shackleton's Legendary Expedition) and the introduction by guy guthridge (manager of nSf's Polar Information Program, now retired after 35 years) speak for themselves.Both speak well of the author and his dedication to putting into print what will add a new dimension to our knowledge of America's "Deep freeze" programs.So what makes this book different from numerous others about the same time period?the value of the book is in its historical content, some of which is included in other works, but it is presented here in an attractive sequence of events.the photographs have excellent resolution and in many respects tell the story of U.S. presence in Antarctica in 1959.historians and Antarctic veterans, both naval and civilian, from this time period will find a great deal to reminisce about, and polar history buffs will also value this book.the map and satellite image of the McMurdo Sound area, Ross Island, and the Dry Valleys in the inside covers are beneficial for sorting out places the author mentions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".