Bibliographic record
Abstract
Pollen stratigraphy from 90 sites in and bordering the Great Lakes record the 5-7 ka history of forest development of the Great Lakes region. By 7 ka beech (Fagus grandifolia) had invaded the oak-hickory (Quercus-Carya) forest of lower Michigan and hemlock (Tsuga canadensis) and beech the white pine (Pinus strobus)-dominated forest of southern Ontario. At the same time, white pine replaced jack pine (P banksiana) as it expanded northward to the Clay Belt beyond its present-day range. Forest changes at 6 and 5 ka were dominated by range extensions of beech and hemlock in a northwesterly direction, by northward expansion of eastern white cedar (Cupressineae), and southward migration of white pine into the Michigan basin. The beech and hemlock migrations (160 m yr-1and 280 m yr-1, respectively) may have been influenced by the cool-moist climate generated by the Nipissing Great Lakes in combination with enhanced regional warming. White pine and eastern white cedar responded to regional warming and reduced precipitation, whereas birch (Betula) and alder (Alnus) may have been influenced more by fire activity caused by the warm-dry climate. The boreal-mixed forest ecotone was displaced 140 km northward at 5-7 ka compared to 60-70 km for the mixed-deciduous forest ecotone.
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 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 | 0.000 |
| 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) | 0.001 | 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".