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Record W4281708916 · doi:10.1017/qua.2022.17

Holocene water levels of Silver Lake, Montana, and the hydroclimate history of the Inland Northwest

2022· article· en· W4281708916 on OpenAlexaboutno aff
Meredith C. Parish, Kyra Clark‐Wolf, Philip E. Higuera, Bryan N. Shuman

Bibliographic record

VenueQuaternary Research · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersColorado Scientific SocietyUniversity of WyomingNational Science Foundation
KeywordsHoloceneGeologyRadiocarbon datingTransectOceanographyHolocene climatic optimumGlacierPhysical geographyHydrology (agriculture)GeomorphologyPaleontologyGeography

Abstract

fetched live from OpenAlex

Abstract The wettest portion of the interior of western North America centers on the mountainous region spanning western Montana, Idaho, British Columbia, and Alberta. Inland ranges there capture the remnants of Pacific storms. Steep east–west hydroclimate gradients make the region sensitive to changes in inland-penetrating moisture that may have varied greatly during the Holocene. To investigate potential hydroclimate change, we produced a 7600-yr lake-level reconstruction from Silver Lake, located on the Montana–Idaho border. Ground-penetrating radar profiles and a transect of four shallow-water sediment cores that were dated using radiocarbon dating and tephrachronology revealed substantial changes in moisture through time. An organic-rich mud unit indicating wet and similar to modern conditions prior to 7000 cal yr BP is overlain by an erosional surface signifying drier than modern conditions from 7000–2800 cal yr BP. A subsequent time-transgressive increase in water levels from 2800–2300 cal yr BP is indicated by a layer of late Holocene muds, and is consistent with glacier expansion and increases in the abundance of mesic tree taxa in the region. Millennial-scale trends were likely driven by variations in orbital-scale forcing during the Holocene, but the regional outcomes probably depended upon factors such as the strength of the Aleutian Low, Pacific sea-surface temperature variability, and the frequency of atmospheric rivers over western North America.

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)0.004
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 score0.012
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.270
Teacher spread0.213 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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