Stephen Taber and the development of North American cryostratigraphy and periglacial geomorphology
Bibliographic record
Abstract
Abstract Stephen Taber's early work on ice segregation and frost heaving was far ahead of its time. His laboratory experiments regarding ice segregation led to our current understanding of frost heave by civil and geotechnical engineers building roads and other structures in cold regions. It also laid the foundation for later process‐oriented field studies of cold‐climate geomorphic processes. Taber's 1943 regional monograph on the origin and history of perennially frozen ground in Alaska, published by the Geological Society of America, was the earliest example of regional cryostratigraphy, and pioneered the regional permafrost and Quaternary studies undertaken later by Katasonov, Popov, Mackay, Péwé, Hopkins, and others. An important dimension of Taber's Alaska work was his application of knowledge gained through laboratory experimentation to the interpretation of ground‐ice exposures in the field. While S. W. Muller is widely regarded as the “father” of permafrost studies in North America, Taber is properly viewed as the “progenitor” of cryostratigraphic studies, although he is not yet widely regarded as such. This study uses archival resources to provide historical context regarding the development of Taber's monograph, to investigate details about the review and publication process it underwent, and to explore the question of why it remains undervalued.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".