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Record W3016369972 · doi:10.1017/qua.2020.12

Minimum limiting deglacial ages for the out-of-phase Saginaw Lobe of the Laurentide Ice Sheet using optically stimulated luminescence (OSL) and radiocarbon methods

2020· article· en· W3016369972 on OpenAlexaff
Timothy G. Fisher, Mitchell R. Dziekan, Jennifer McDonald, Kenneth Lepper, Henry M. Loope, Francine M.G. McCarthy, B. Brandon Curry

Bibliographic record

VenueQuaternary Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsBrock University
Fundersnot available
KeywordsMoraineRadiocarbon datingGeologyIce sheetLimitingHoloceneLobeGeomorphologyPaleontologyOceanographyGlacial period

Abstract

fetched live from OpenAlex

Abstract Twenty-four new optically stimulated luminescence (OSL) and radiocarbon ages from sediment cores in nine lakes associated with the Shipshewana and Sturgis moraines in northern Indiana and southern Michigan estimate when recession of the Saginaw Lobe of the Laurentide Ice Sheet was underway in the southern Great Lakes region, USA. Average OSL ages of 23.4 ± 2.2 ka for the Shipshewana Moraine and 19.7 ± 2.2 ka for the Sturgis Moraine are considered minimum limiting deglacial ages for these recessional moraines. The much younger radiocarbon ages are consistent with other regional radiocarbon ages from lakes, and record climate amelioration around ~16.5 cal ka BP. Early recession of the interlobate Saginaw Lobe was well underway by 23.4 ± 2.2 ka, when the adjacent Lake Michigan and Huron-Erie lobes were a few hundred kilometers farther south and near their maximum southerly limits. The results provide the first time constraints when sediment from the Lake Michigan and Huron-Erie lobes began filling the accommodation space left by the Saginaw Lobe. The difference between the oldest radiocarbon and OSL age is 7400 yr for the Shipshewana Moraine and 3400 yr for the Sturgis Moraine.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.910
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.187
GPT teacher head0.429
Teacher spread0.243 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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