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Record W3186103167 · doi:10.1029/2021gl093919

Catastrophic Drainage From the Northwestern Outlet of Glacial Lake Agassiz During the Younger Dryas

2021· article· en· W3186103167 on OpenAlexafffund
Sophie L. Norris, Daniel García‐Castellanos, John D. Jansen, Paul A. Carling, Martin Margold, Robin Woywitka, Duane Froese

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMacEwan UniversityUniversity of AlbertaDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of AlbertaUniverzita Karlova v Praze
KeywordsYounger DryasMeltwaterGlacial lakeGeologyGlacial periodOceanographyFlood mythPhysical geographyClimatologyGeomorphologyArchaeologyGeography

Abstract

fetched live from OpenAlex

Abstract Catastrophic meltwater drainage from glacial Lake Agassiz has been hypothesized as a trigger for large‐scale ocean circulation change initiating the Younger Dryas cold reversal. Here we quantify the flood discharge that formed the northwestern outlet of Lake Agassiz using a one‐dimensional step‐backwater model and a zero‐dimension gradual‐incision model. Applying these two independent models, we estimate a peak discharge range of 1.8–2.5 × 106 m3 s−1 and a flood volume of ∼21,000 km3. Such a discharge can only be derived from Lake Agassiz rather than one of the two smaller regional glacial lakes: Churchill or Meadow. When coupled with existing ice margin chronologies, these results demonstrate that the northwestern outlet of Lake Agassiz provides a viable link for catastrophic meltwater to drain to the Arctic Ocean over a 6–9 month period during the Younger Dryas, though it is unclear whether this was near its beginning.

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 score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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

Citations45
Published2021
Admission routes2
Has abstractyes

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