MétaCan
Menu
Back to cohort

Timing of Cordilleran-Laurentide ice-sheet separation: Implications for sea-level rise

2024· article· en· W4392119688 on OpenAlexafffund
Alberto V. Reyes, Anders E. Carlson, Jorie Clark, Louise Guillaume, Glenn A. Milne, Lev Tarasov, Elizabeth C. B. Carlson, Feng He, Marc W. Caffee, Klaus M. Wilcken, Dylan H. Rood

Bibliographic record

VenueQuaternary Science Reviews · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of NewfoundlandUniversity of OttawaUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAustralian Nuclear Science and Technology OrganisationNational Science Foundation
KeywordsMeltwaterGeologyIce sheetDeglaciationOceanographyIce streamArctic ice packSea iceGeomorphologyGlacial periodHoloceneCryosphere

Abstract

fetched live from OpenAlex

During the last deglaciation, collapse of the saddle between the North American Cordilleran and Laurentide ice sheets led to rapid ice-sheet mass loss and separation, with meltwater discharge contributing to deglacial sea-level rise. We directly date ice-sheet separation at the end of the saddle collapse using 64 10Be exposure ages along an ∼1200-km transect of the ice-sheet suture zone. Collapse began in the south by 15.4 ± 0.4 ka and ended by 13.8 ± 0.1 ka at ∼56°N. Ice-sheet model simulations consistent with the 10Be ages find that the saddle collapse contributed 6.2–7.2 m to global mean sea-level rise from ∼15.5 ka to ∼14.0 ka, or approximately one third of global mean sea-level rise over this period. We determine 3.1–3.6 m of the saddle collapse meltwater was released during Meltwater Pulse 1A ∼14.6-14.3 ka, constituting 20–40% of this meltwater pulse's volume. Because the separation of the Cordilleran and Laurentide ice sheets occurred over 1–2 millennia, the associated release of meltwater during the saddle collapse supplied a smaller contribution to the magnitude of Meltwater Pulse 1A than has been recently proposed.

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)0.002
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 score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.151
GPT teacher head0.395
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 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

Citations6
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueQuaternary Science ReviewsSame topicGeology and Paleoclimatology ResearchFrench-language works237,207