Quantifying Pleistocene loess provenance in midcontinental North America using a mixing model: Implications for glacial lobe evolution along the southern Laurentide ice sheet
Bibliographic record
Abstract
Abstract Quaternary glaciations have significantly impacted the midcontinental North American landscape, leaving behind a proglacial sediment record of glacial lake deposits, glacio-fluvial sand and gravel, and windblown loess. This paper aims to expand upon a previous study using detrital zircon provenance analysis to investigate midcontinental North American loess provenance in relation to the glacial history of the southern Laurentide ice sheet during the middle to late Pleistocene. The study incorporates previously published detrital zircon data from last glacial tills (representing the Huron-Erie Lobe, Lake Michigan Lobe, Green Bay Lobe, Superior Lobe, Des Moines Lobe, and James Lobe), middle to late Pleistocene loess from several sites along the Illinois and Mississippi River valleys, and suspended sediment load inputs from the Missouri River and Arkansas River. A statistical mixing model (DzMix version 2.2) was used to estimate the relative proportions of glacial and nonglacial sources to Wisconsin Episode, Illinois Episode, and pre–Illinois Episode age loess. We show that mixing models that include the modern suspended sediment loads of the Missouri and Arkansas Rivers significantly improve (up to 23% increase in cross-correlation value) the source characterization of Wisconsin Episode (last glacial) and Illinois Episode (penultimate glacial) loess deposits within the Mississippi River drainage basin. These river sources are dominant inputs for certain loess sites (as much as 51%), but their relative contributions are not static across time and space, which has implications for temporal and spatial differences in relative sediment source estimates and glacial and fluvial sediment transport evolution. Specifically, differences in relative sediment proportion estimates among Wisconsin, Illinois, and pre–Illinois Episode loess support previous evidence for the persistence of a Quebec-Labrador ice dome source through multiple glacial cycles, even with the inclusion of river sources in updated mixing models. Illinois and pre–Illinois Episode loess in the southern portion of our study area received ~20% detrital zircon input from sources similar to the present-day Missouri and Arkansas Rivers, suggesting that these river systems were contributing a significant amount of detritus to the lower Mississippi River valley region during the middle Pleistocene.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".