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Record W3093707810 · doi:10.1130/2017.0048(04)

The Mesoproterozoic Belt Supergroup in Glacier and Waterton Lakes national parks, northwestern Montana and southwestern Alberta: Sedimentary facies and syndepositional deformation

2017· book-chapter· en· W3093707810 on OpenAlexaffabout
Brian R. Pratt

Bibliographic record

VenueGeological Society of America eBooks · 2017
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeologyFaciesSupergroupSedimentary depositional environmentSedimentary rockLithologyStructural basinPaleontologyGlacierGeochemistry

Abstract

fetched live from OpenAlex

ABSTRACT A large portion of the Belt-Purcell Supergroup is well exposed in the vicinity of Glacier and Waterton Lakes national parks of northwestern Montana, USA, and southwestern Alberta, Canada. These strata were deposited in the northeastern part of the Mesoproterozoic Belt Basin. The dramatic rate of subsidence combined with dominantly fine-grained sediment influx produced thick units of broadly uniform lithology, which constitute the spectacular and unusually colorful mountain scenery of this region. Seemingly fairly simple at first glance, in detail these rocks exhibit a great deal of facies heterogeneity and a number of unusual attributes. This has resulted in contrasting and controversial interpretations of sedimentary features, depositional dynamics, sedimentary environments, and consequently the overall understanding of the entire basin. The Belt Basin reveals itself to be a unique setting in many respects, but ideas stemming from these rocks have implications for other strata, not just those of pre-Cambrian age, but for the entire Phanerozoic as well. The Belt Supergroup is therefore a particularly stimulating field-trip destination that challenges textbook interpretations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.660

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.197
Teacher spread0.184 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2017
Admission routes2
Has abstractyes

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