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Record W4405005913 · doi:10.1017/qua.2024.34

South-central Laurentide Ice Sheet dynamics and the formation of proglacial Lake Vita during MIS 3

2024· article· en· W4405005913 on OpenAlexaffabout
Michelle S. Gauthier, Tyler Hodder, April S. Dalton, Vanessa Brewer, Olav B. Lian, Sarah A. Finkelstein, Maria Schaarschmidt, Alessandro Mereghetti

Bibliographic record

VenueQuaternary Research · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à MontréalManitoba HydroUniversity of TorontoUniversity of the Fraser ValleyUniversity of Waterloo
Fundersnot available
KeywordsGeologyIce sheetGeomorphologyOceanography

Abstract

fetched live from OpenAlex

Abstract An understanding of the growth and demise of ice sheets over North America is essential to inform future climate models. One poorly studied subject is the glacial dynamics during interstadial Marine Isotope Stage (MIS) 3 (57–29 ka). To better constrain the southern margin of the Laurentide Ice Sheet during this time period, we re-examined a stratigraphic sequence in southeast Manitoba, Canada, and provide robust evidence for advance and retreat of ice. Around 46.6 ± 5.1 ka (1σ error), fluvial sands were deposited under similar precipitation and significantly cooler summer temperatures than present-day. Ice then advanced south over the area, before retreating once again and a return to boreal forest and grassland conditions. The area was then covered by proglacial Lake Vita, dammed by ice to the north. Geochronology constraints indicate Lake Vita existed from ca. 44.3 ± 3.6 to 30.4 ± 2.3 ka (1σ error), although gaps in the optical and finite radiocarbon ages suggest either a lack of data or plausible temporary ice-margin advances during this time period. Ice covering most of Manitoba during MIS 3 is in line with global δ 18 O records, and glacially influenced sediment deposition in the Mississippi River basin.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.291
Teacher spread0.262 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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