Inland dunes on the abandoned bed of Glacial Lake Chicago indicate eolian activity during the Pleistocene-Holocene transition, southwestern Michigan, USA
Bibliographic record
Abstract
Abstract Inland dune fields have recently emerged as a source of data for reconstructing paleoenvironments and climate in the western Great Lakes region of North America during the Pleistocene-Holocene transition. We employ optically stimulated luminescence (OSL) methods, radiocarbon ages, soils, and landform relationships to determine the age of inland dunes in Ottawa County, Michigan. These dunes rest on the abandoned bed of Glacial Lake Chicago, which is thought to have been exposed after ~13.6 ka. OSL analyses from two inland dunes yield ages ranging from 13.3±1.1 to 11.6±0.9 ka (uncertainty=2σ). Fine sand in the parabolic dunes suggests deflation of exposed glaciolacustrine nearshore sand by northwesterly and westerly winds. These new data add to a growing number of studies that demonstrate widespread eolian activity in the western Great Lakes region during the Pleistocene-Holocene transition. OSL ages from dune fields in the western Great Lakes indicate peak eolian activity and dune stabilization occurred during or following the Younger Dryas and Preboreal events. Northwesterly and westerly winds suggest the limited effect of hypothesized easterly anticyclonic winds during the Pleistocene-Holocene transition. Rapidly changing climate and newly deglaciated surfaces provided an ideal environment for dune formation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".