Convergent Adaptive Strategies During the Transitional Holocene: A Case Study from the Alpena-Amberley Ridge in the Great Lakes Region
Bibliographic record
Abstract
The Pleistocene-Holocene Transition (PHT) (13,000–8,000 years ago) was a period of pronounced climatic and landscape change that affected every continent. Along with retreating glaciers, rising sea levels produced dramatic effects on landscapes around the world. In response, foraging groups from different portions of the globe developed convergent adaptive strategies, including miniaturized stone tool industries, niche construction, increased mobility, and far-reaching social networks. This dissertation investigates the global phenomenon of convergent adaptive strategies during the PHT through a case study from the Alpena-Amberley Ridge (AAR). The AAR is a limestone and dolomite landform that crosses the Lake Huron Basin from northwest to southeast, forming a causeway that connects both sides. The landform served as a land bridge that caribou likely used on seasonal migration routes and that ancient foragers used for seasonal hunting. The AAR is currently underwater and was only traversable, dry land during the Lake Stanley lowstand between about 11,000 and 8,000 years ago. The submerged context of the AAR has preserved archaeological data, including organic remains and landscape modifications such as hunting blinds and drive lines, from disturbance by modern development. This makes it an ideal location for investigating hunter-gatherer adaptations to new and changing conditions at the onset of the Holocene. The results of this investigation show that foraging strategies on the AAR resemble global patterns but also exhibit unique expressions strongly influenced by the local environment. These adaptations most closely resemble those documented from northern post-Arctic landscapes in western Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".