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Record W3138572664 · doi:10.1002/ppp.2096

Stephen Taber and the development of North American cryostratigraphy and periglacial geomorphology

2021· article· en· W3138572664 on OpenAlexaff
Frederick E. Nelson, Hugh M. French

Bibliographic record

VenuePermafrost and Periglacial Processes · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of VictoriaUniversity of Ottawa
FundersNational Science Foundation
KeywordsContext (archaeology)PermafrostGeologyFoundation (evidence)Frost heavingPhysical geographyArchaeologyGeomorphologyGeographyPaleontologyOceanography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.227
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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