Paleobotanical and environmental implications of a buried forest bed in northern Lower Michigan, USA
Bibliographic record
Abstract
Sediments and stratigraphy at the Tower Buried Forest site provide a glimpse into the immediate post-glacial environment of northern Lower Michigan, USA. At this site, sandy glacial outwash is overlain by (1) ≈37 cm of peat associated with a wetland, above which are (2) flat-lying spruce and larch logs and branches that date between ≈10 910 and 10 340 cal years BP. Above the woody materials are ≈55–75 cm of sand, interbedded with organic muck laminae, which we interpret as local alluvium. This stratigraphic sequence is overlain by muck soil materials associated with the modern wetland. Pollen and plant macrofossil analysis of the lower peat indicate that a tundra–boreal parkland had been established here, shortly after final deglaciation. Later, Picea glauca (white spruce) , Picea mariana (black spruce), Larix laricina (larch), and Abies balsamea (balsam fir) became abundant in an open boreal forest – Spaghnum peatland. Subsequent increases in Pinus (pine) and Typha (cattail) indicate drying and possibly warming conditions. At ca. 10 340 cal years BP (or a few centuries prior), nearly two millennia after final deglaciation, water flowed across the site, possibly knocking over the trees.. Interbedded sand and muck deposits above the flat-lying logs are interpreted as alluvial deposits from this event, after which the wetland became quiescent again.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".