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Record W6986317282

Palaeogeographical reconstruction and hydrology of glacial Lake Purcell during MIS 2 and its potential impact on the Channeled Scabland, USA

2020· article· en· W6986317282 on OpenAlexfundaboutno aff

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeglaciationMeltwaterSedimentary rockGlacial periodShelf iceFlood mythGlacial lakePost-glacial rebound
DOInot available

Abstract

fetched live from OpenAlex

Large, ice‐marginal lakes that were impounded by the maximally extended Cordilleran Ice Sheet (CIS) provided source waters for the extraordinarily large floods that formed the Channeled Scabland of Washington and Idaho, USA. However, flood flows that drained CIS meltwater and contributed to landscape evolution during later stages of deglaciation have hitherto been poorly investigated. This paper provides the first evidence for such a late deglacial floodwater source: glacial Lake Purcell (gLP). Sedimentary evidence records the northward extension of gLP from Idaho, USA into British Columbia, Canada and establishes its minimum palaeogeographical extent. Sedimentary evidence suggests that the deglacial Purcell Lobe was a capable ice dam that impounded large volumes of gLP water. A review of glacio‐isostatically affected lakes during CIS deglaciation suggests that gLP could have been subjected to tilts ranging from 0 to >1.25 m km−1. Sedimentary evidence suggests high lake plane tilts (⪆1.25 m km−1) are the most likely to have affected gLP. Using this, the palaeogeography and volume of gLP are modelled, revealing that ~116 km3 of water was susceptible to sudden drainage into the Channeled Scabland via the Columbia River system. This calculation is supported by sedimentary and geomorphic evidence compatible with energetic flood flows along the gLP drainage route and suggests gLP drained suddenly, causing significant landscape change.

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 score0.027
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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