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Record W2029355993 · doi:10.1177/0959683614540948

New ages for shoreline stumps along Lake Winnipeg, Canada, and their implications for paleo-lake level estimates

2014· article· en· W2029355993 on OpenAlexaffabout
Scott St. George, Max C. A. Torbenson

Bibliographic record

VenueThe Holocene · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsSubfossilShoreRadiocarbon datingGeologyHoloceneStructural basinPaleolimnologyPhysical geographyArchaeologyOceanographyHydrology (agriculture)GeographyPaleontology

Abstract

fetched live from OpenAlex

As the largest body of water on the northern Great Plains of North America, Lake Winnipeg in central Manitoba, Canada, is crucial to the region’s hydrology, economy, and society. Previous research identified exposed subfossil stumps at several locations along the shore in both the lake’s north and south basins, and interpreted them as evidence of low lake levels c. ad 1650 caused in part by reduced inflows. Here, we report new radiocarbon dates for submerged stumps rooted in the foreshore of Lake Winnipeg near the Spider Islands, which are located in the lake’s northeastern sector close to its outlet. The 11 stumps had calibrated ages ranging between 2880 and 4150 cal. yr BP, which implies the establishment and mortality of these trees had no connection to Lake Winnipeg and instead that they grew, died, and were preserved within a forest located several hundreds of meters inland. If these new dates are correct, they argue against a simple hydrological explanation for these submerged trees. Instead, our results suggest these trees died 3–4 kyr ago and are now exposed because of gradual, isostatically driven changes in the basin configuration and shoreline position of Lake Winnipeg. Because they date to the mid- or late Holocene, we conclude these subfossil stumps do not constitute clear evidence of hydrologically caused low lake stands in Lake Winnipeg or widespread drought on the northern Great Plains.

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)0.001
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 score0.910
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.266
Teacher spread0.224 · 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
Published2014
Admission routes2
Has abstractyes

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