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Record W4243749761 · doi:10.1002/essoar.10507476.1

Seismic reflection and electrical resistivity imaging reveal pre-Quaternary glaciation in the Rocky Mountains (Unaweep Canyon, Colorado)

2021· preprint· en· W4243749761 on OpenAlexaff
Anna Patterson, Michael Behm, Werner Chwatal, Adrián Flores Orozco, Yichuan Wang, Gerilyn S. Soreghan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCanyonGeologyPreprintQuaternaryArchaeologyWorld Wide WebComputer sciencePaleontologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Unaweep Canyon (Uncompaghre Plateau, Colorado) represents an enigmatic landscape with a complex evolution. Interpretations for its origin have ranged from ancestral fluvial erosion in the late Cenozoic to glacial erosion in the Paleozoic, or some combination thereof, with significant implications for global climatic and large-scale tectonic reconstructions. To address the conflicting interpretations, we acquired a high-resolution seismic reflection profile to investigate the depth, structure, and sedimentary infill in the canyon. The dataset is further complemented with a high-resolution electrical resistivity survey. Integrated with other geophysical and geological data, the results unambiguously demonstrate an overdeepened Precambrian basement with pronounced transverse U-shape and corroborate the hypothesis of a pre-Quaternary glacial origin. Our data constitute the first detailed and high-resolution image of a buried pre-Quaternary glacial valley in North America, and thus have far-reaching implications for our understanding of global ice houses as well as the tectonic conditions enabling preservation of such systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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